Data mining and machine learning studies on the subject “defect prediction.”
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These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
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IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\nInitially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\nThese books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
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Despite major advances over the past three decades many patients come late to treatment and find that the therapy is incompletely addressed to the complex psychopathology and co-morbidities of the illness. The present book brings timely and contemporary understandings of bulimia nervosa to aid in current thinking regarding prevention and treatment. It will be read by therapists interested in enhancing their current approaches and those interested in earlier and more effective prevention and closing the gap between illness onset and accessing treatment. 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\r\n\tThe main objective of the present volume is to diffuse the latest information related to the Auditory Hearing system from the periphery to the Auditory cortex with a strong emphasis on the Vestibular system and the various types of hearing problems associated with otolith dysfunction. The sections will include material on the current hearing assessment protocols and technologies. Sections of the book are also dedicated to hearing loss, imbalance in the elderly, hearing restoration strategies (old & novel) including cochlear implants and hearing aids.
\r\n\r\n\tThe book has been edited with a strong educational perspective (all chapters include an extensive introduction to their corresponding topic and an extensive glossary of terms). The book will contain material suitable for Graduate students in Audiology, ENT, Hearing Science, and Neuroscience.
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Hector Ceballos Lascurain coined the term, “Ecotourism” in 1983 to describe the nature-based travel to relatively undisturbed area with an emphasis on education [1]. There is no such universally accepted definition of ecotourism. It is differentiated from nature-based tourism as it helps in sustainable rural development and makes biodiversity conservation economically viable for local communities [2]. Ecotourism is one of the preferred tools for conservation and community development in many rural areas. Its effectiveness depends in its potential to provide local economic benefits by maintaining ecological resource integrity through low-impact and non-consumptive use of local resources [6].
\nEcotourism is an alternative form of tourism which embraces tourism in the biophysical environment in natural areas. It incorporates ecologically sustainable activities, conservation supporting measures and involvement of local communities [7]. It neoliberalises nature and focuses on capitalist development, community development, poverty alleviation, wildlife conservation and environmental protection [4]. Traditional economic bases like agriculture, livestock and hunting are not compatible with protected territories so change in traditional economic activities can be done by shifting cultural attitudes [8], towards ecotourism enhancement [9].
\nEcotourism provide jobs for the local people and a market for local products. It encourages cultural sensitivity in guest–host relations and acts as a catalyst for ecologically sustainable development. But, planning is required to address the issues of ownership, management and coordination of protected areas. It enhances wildlife conservation and equitable sharing of benefits from ecotourism [10]. It helps in the conservation of natural, cultural and built resources and maintains the quality of life of local area [11]. With an objective of environmental conservation, it creates sustainable economic development and balances the conflicting goals of economic development and biodiversity conservation [2,5,12]. Considering the sustainable principles and practices, it fulfills goals of biodiversity conservation, poverty reduction and business viability [13]. In ecotourism, local people realize the importance of conservation and protect the environment in active manner. They maintain national standards of atmospheric quality, sound quality, drinking water, sewage, lampblack and all kinds of establishments [14].
\nCommunity-based ecotourism is the best option in an area which is conserved, owned and managed by a community to gain income by operating a tourism enterprise [1]. There will be direct and indirect participants and beneficiaries looking after environmental conservation, business enterprise and community development [1]. Local income, biodiversity conservation and cultural preservation are necessary for tourism intervention in such areas. Successful implementation of such ecotourism requires improvement of accessibility, liberalization of pricing policy, empowerment of community, development of tourism facilities and incorporation of nature-based tourism products [15]. Community-based ecotourism is associated with basic accommodation and facilities marketed for independent and low-budget travelers who do not expect high-end tourism facilities. There is high degree of public participation but sometimes exclude interests of communities and benefits narrow elite. Wildlife and landscapes are sold in multiple ways as images, products and destinations in such tourism [4].
\nEcosystem tourism is supply-led tourism strategy that enables sustainable development with the central theme of ecosystem integrity. It does not exclude tourist and commercial exploitation of the ecosystem but the entire ecosystems or the specific biological diversity are managed in relation to the tourism experience. The main resource base or capital stock for tourists is the ecosystem which is the assemblages of organisms, physical environment and an array of interactions and feedbacks. In such ecotourism, the ecosystem is able to absorb or adapt the pressures of tourists until it develops a more urban character [16].
\nRural tourism is a form of tourism originated in Europe which takes place in rural environment based on the rural resources reflecting rurality, traditional folkcustom and agricultural products. In such tourism, tourists enjoy staying in the yard, strolling and looking around flowers, green plants, gardens, old architecture and playing cards. It enhances the profit from agriculture and provides peasantry with more profit, more employment opportunities and better living conditions by combining agriculture and tourism. It plays a positive role in prompting the development of rural economy and rural culture. Rural tourism is in a primary stage by giving priority to agricultural sightseeing, farming experience and close quarters leisure with accommodations and simple entertainment [14].
\nCultural tourism preserves cultural traditions and enriches the quality of the life of local communities. It is necessary to provide education and training programs of ecotourism and nature reserve for tourism operators, employees and tourist to develop and promote minority cultures and cultural traditions. Research opportunities should be provided to attract scientists and students to conduct research in the region [17].
\nNature-based tourism is a large growing global industry which depends upon the natural environment occurring in parks and protected areas. It depends on appropriate levels of environment quality and suitable levels of consumer service. Nature-based tourism has been a key component of tourism industry in several countries [18].
\nProtected area-based tourism in parks and protected areas constitute a significant proportion of international tourism in developing countries. It has generated substantial revenue for the state and improved the local livelihoods which are directly dependent on tourism. Many state governments and international conservation agencies provide sufficient funds for the establishment of protected areas and their long-term viability. Countries such as Kenya, Zimbabwe, Tanzania, Costa Rica and Nepal are only a few examples where protected area tourism constitutes a significant proportion of international tourism [11].
\nThere is nature-based tourism in Australia, Tanzania and New Zealand. The national ecotourism strategy for Australia had focused on ecotourism in regional areas to generate foreign exchange earnings, employment, and other economic and social benefits. For the aggressive and successful policy development in the country, Australia is taking advantage with its spectacular and diverse natural features, unique flora and fauna, and diverse cultural heritage [18].
\nAfter the coronation of 1974 in Bhutan, small groups of tourists were allowed into the country and given permission to visit dzongs and goembas in Thimphu and Paro. Government established a quota of 200 tourists a year in which tourist have to travel in a group of six or more. The cost was set at US$130 per day and all the guests had to stay in government-approved hotels, guesthouses and lodges. Bhutan’s rich heritage and vibrant culture has its distinctive traditions and customs demonstrated in its religious festivals and architecture. Towering crags and highly diverse forests is also home to takin, snow leopard, golden langur, red panda, tiger and elephant. Trained trekking guides are provided periodic knowledge and services to accompany trekking groups. Gross expenditure of US$14 million provides significant foreign exchange earnings in Bhutan. The present policy consists of a fixed price set by government with quality of service and visitor experience maintained by market share competition between tour operators and government regulation. Ecotourism in Bhutan was able to limit environmental and cultural impact of Western countries. But, a large number of Indian tourists would cause a major challenge in maintaining cultural and environmental carrying capacity [19].
\nIn Small Islands of the South Dodecanese, local communities contribute in ecotourism program and help to minimize the conflicts. The islands’ geomorphology and geographic position provides opportunity in nature trekking, mountain climbing and sailing. It contributes to the elongation of the tourist period and the increase in tourist revenues. Adverse ecological effects of adventure tourism activities such as underwater fishing by free-divers are negligible [20].
\nEcotourism is already the largest source of foreign exchange in countries like Costa Rica and Guatemala. It is one of the meaningful sources of economic development and job creation. Cross country evidence demonstrates that tourism is labor intensive and offers a variety of small scale opportunities creating jobs for poor, women, young and indigenous communities [1].
\nCommunity-based ecotourism in southern Thailand is only partially successful. The benefits of ecotourism in Phuket are more than the costs in terms of community development. Environmental sensitivity and responsibility promoted by ecotourism can increase the political, economic, social and environmental interests of host communities. The relationship between community-based ecotourism and empowerment is complex and incomplete. The economic and psychological empowerment of individuals is accomplished by the political and social empowerment of communities [21]. Tourism operators in countryside of Thailand had also focused in nature-based tourism [11].
\nIn Kuscenneti National Park of Turkey, the park administration encouraged local bodies to organize youth camps in the park along with some additional facilities. By allowing nearby communities to provide infrastructure and services to visitors, visitor expenditures are able to flow more easily into the local economy. Locals have positive views towards tourism development and have established some restaurants and guest houses. These activities have provided sources of extra income for families who depend on traditional activities. Economic benefits of tourism have changed the attitudes of local communities and there is good relationship between the national park and the local community [22].
\nTourism in Kerala was promoted as a major agenda by government for employment opportunities and foreign exchange earnings. Accommodation industry provides more subsidies, tax cuts, concessions, promotional packages and soaps which contributes to the growth and development of the economy. In spite of the positive benefit to the economy, tourism projects have adverse environmental effects [23].
\nImplementation of Sitakunda Botanical Garden and Eco-park project in Chittagong, Bangladesh, had enriched the area with natural regeneration, new plantations, infrastructure development, new destination for visitors and nature-based tourism. Tourism is also providing revenue to the park authority and income to local people [24].
\nTourism had provided alternative mechanism of resource utilization in China’s nature reserves but it is causing adverse impact on environmental resources. Litter problems, water pollution, noise pollution and air pollution are the major problem occurring in nature reserves [25]. Local government officials had a great role for conservation, development and logistical functions towards tourism development in Wuzhishan Mountain Region of China. Participation of local communities and cooperation between local governments, local communities, NGOs and the private sector was important for development and management of the nature reserve [17].
\nLugu Lake region is one of the most important areas for ecosystem conservation and cultural heritage protection in China [26]. Shangri-La County in China has abundant tourism resources and products but the natural resources were exploited beyond the carrying capacity. There is need for sustainable use of tourism resources, proper handling of tourism demands, repartitioning tourism flow and digital system for tourism management [27]. Appropriate measures by government were conducted in Xishuangbanna Biosphere Reserve (XBR) to protect the core area from over-exploitation and unsustainable human use. But, simple life and material needs of traditional local people is vulnerable to Western influence [28]. Visitors as a knowledge seeker, leisure traveler and nature lover are preferring good-quality information, tour guides and low impact activities. They are selecting the destination with high ecological value and good site management in the case of Hong Kong [29].
\nEcotourism as a component of green economy contributes greatly for Gross Domestic Product (GDP) for government and private organizations [30]. Developing countries such as Nepal have advantage in ecotourism as they have unique natural environment and culture. It generates employment for the unskilled workforce in rural area through community-led tourism activities for preserving natural ecosystems [3].
\nNepal is a small country with an area of 147,181 sq. km., having great diversity of topographic and eco-climatic features rich in natural and cultural heritage. It is renowned for its physiographic and eco-climatic variations, Himalayan ranges, natural beauty, protected areas, rich bio-diversity, spectacular landscape, extraordinary cultural heritage and mosaic of ethnic diversity [31]. It is one of the most adventurous cultural and ecotourism destinations in the world which depends on the quality of the natural environment [11,32]. Its main attractions are the highest mountain range in the world and many cultural and natural attractions [32]. There are many trekking routes and sites for ecotourists to explore natural beauty throughout Nepal ranging from the Kanchenjunga Conservation Area and Illam in the east to Khaptad and Shuklaphanta National Park in the west [5].
\nNepal has a total population of over 23 million and consists of 59 ethnic groups and 101 spoken languages. It is the birthplace of Lord Buddha, the Light of Asia. It has a rich cultural heritage, where more than 1250 heritage sites have been identified and documented from 72 districts. Within Kathmandu valley, Kathmandu Development Committee has listed a total of 870 religious and cultural shrines and monuments. It consists of eight World Cultural Heritage Sites: Bhaktapur, Patan and Kathmandu Durbar Squares; Swayambhunath; Baudhanath; Pashupatinath; Changunarayan and Lumbini (the birth place of Lord Buddha) combined with two world’s Natural Heritage Sites: Sagarmatha and Chitwan National Parks [31].
\nThese resources are the major attractions for the foundation and acceleration of tourism industry in Nepal. It is necessary to manage these tourism resources properly by mobilizing the local participation with sufficient considerations on the quality of supply side of tourism in order to attract the maximum number of tourists from different parts of the world. Nepal is one of the countries that receive the largest number of international tourists with rapid growth of tourism [31]. Trekking and mountaineering are creating opportunities in the operation of tea houses and lodges along the trails [33], which has direct impact on livelihoods [5,34–37]. Nepal Government has also identified ecotourism as a strong sector contributing significantly to environmental conservation, employment generation and socioeconomic development [38].
\nCommunity-based rural tourism in protected areas had supported livelihood of local communities by providing opportunities to the national and international visitors in community activities [11,38]. So, Nepalese government has developed national parks, wild life reserves, buffer zones, conservation areas and cultural heritage sites for conserving wildlife and enhancing ecotourism [39]. Ecotourists enjoy its Himalayan ranges, natural beauty, protected areas, biodiversity, landscape, cultural heritage and ethnic diversity [31]. Having 8 out of the 14 high mountains over 8000 m elevation in the world is also making Nepal a main source of tourist attraction [32]. Tourism had already been an alternative source of income generation in the villages of Kaski, Tanahu, Syangja, Lamjung and Gorkha districts in Western Nepal through rural tourism initiatives [5,38].
\nTourist can enjoy scenic beauty, trek and Climb Mountains, view Sagarmatha and encounter wilderness in Sagarmatha National Park (SNP). Ecotourism and its sustainability in SNP require the inclusion of local participation, economic viability, education dissemination, tourist satisfaction and tourism impact reduction [32].
\nAnnapurna region is one of the world\'s most popular trekking destinations which comprises a variety of ecosystems from sub-tropical lowlands and temperate forest to some of the world\'s highest alpine peaks [7]. To save the region’s biological diversity from growing environmental crisis, conservation area was set up handling management by government to National Trust for Nature Conservation (NTNC), formerly King Mahendra Trust for Nature Conservation (KMTNC) [11]. Considering local people as a custodian of natural and cultural heritage, participatory natural resources conservation method was implemented for making project financially sustainable [11]. With a goal to lessen the adverse impacts and strengthen the positive benefits of tourism, bottom up approach based on local management was implemented in Annapurna Conservation Area Project (ACAP) [7]. While trekking through Annapurna Conservation Area (ACA), visitors can enjoy hiking, mountaineering, viewing wildlife and visiting cultural sites, ethnic museums and natural photography [39]. For this, ACAP collect entry fees from international tourist for community development projects on health and sanitation, education, environmental protection, etc. [11]. Also, to increase community ownership and participation, management committees have been formed which enhance ecotourism for conservation and development activities by implementing fuel-efficient stoves, back boilers, kerosene and electricity [5,7].
\nHomestays in Barpak, Nepal, are helpful in addressing socioeconomic, political, ethnic and gender disparities. They have experienced cleaner pathways, yards and junctions with shops full of a variety of cleaner products, water taps and public toilets [38].
\nTotal number of tourist arrival in Nepal in 2012, 2013 and 2014 was 803,092, 797,616 and 790,118, respectively. Annual growth rate of tourists during these years was 9.1, -0.70 and -0.95 and their average length of stay was 12.16, 12.51 and 12.44, respectively. But from 2009 to 2012, annual growth rate of tourist and average length of stay was in increasing trend. Out of total tourist arrival in Nepal, 57.8 percent tourist arrives for the purpose of holiday in 2011 and this trend decreases to 47.3 percent in 2012. In 2013, tourist arriving for the purpose of holiday was again increased to 51.5 percent. The arrival of tourist for trekking and mountaineering, business, pilgrimage, official and other purpose was 12.8, 3.5, 9.0, 4.7 and 6.8 percent, respectively in 2013 [40].
\nDifferent studies are conducted to assess the issues of ecotourism. There is a need to find out the issues raised in these researches and find out the methodologies applied by them. It would help the researchers to find the subject of research with appropriate methodology. It would be easier and faster to select appropriate literature for them. This part of the chapter reviews the location, aim and methodology of different researches throughout the world.
\nNianyong and Zhuge [25] conducted a study in China’s nature reserves to assess the opportunities and challenges of ecotourism. Questionnaire survey was implemented between July and December 1997 to identify broad issues on ecotourism development in China’s nature reserves.
\nSchellhorn [41] conducted a study in Indonesian Island of Lombok to understand the constraints and barriers of ecotourism in indigenous communities with the help of census survey of the local hospitality sector, semi-structured interviews with local tourism stakeholders, participant observations and analytical mapping of tourism infrastructure and services.
\nBertella [42] conducted a study in Norway with an objective to assess the challenges and the critical factors for the development and management of wildlife tourism based on the knowledge of natural sciences. The study was conducted with the help of secondary data from the internet and primary data through informal conversations and semi-structured interviews with the firm leader in June and August 2009.
\nBecken and Clapcott [43] conducted a study in Fiji and New Zealand with the help of a research to make appropriate policy to cope with impacts of climate change. Stakeholders of climate change, tourism and both the field were involved in policy-making process.
\nOkello [44] conducted a research in Kuku Community Conservation Area (KCCA) in Kenya to assess ethical, ecological, economic and management issues at community level with the help of semi-structured questionnaire to visitors and tour companies.
\nDegang and Xiaoting [14] conducted a study in Nongke Village of Chengdu City to assess the concepts and criteria of rural tourism and ecotourism with the help of field work, small-scaled informal discussion, unstructured interviews and data from local tourism management.
\nGenzong et al. [17] conducted a research in Wuzhishan Mountain Region of China to identify the information that incorporates ecotourism related values. Sixty-seven questionnaires were administered to assess how the local government officials saw the development of tourism as an important priority in the region through four phases: profiling the community, analyzing trends, creating the vision, and developing an action plan.
\nHitchner et al. [45] conducted a study in Kelabit Highlands of Sarawak, Malaysia, and the Kerayan Highlands of Kalimantan, Indonesia to examine the current state of community-based transboundary ecotourism. The study was conducted with the help of interviews with local guides, homestay owners, urban-based tour operators, tourism promotion centers and agencies; intercommunity dialogues regarding transboundary ecotourism and analysis of promotional materials on ecotourism; comments in the visitors’ books of lodges, tourists’ websites and travel blogs.
\nBeaumont [46] conducted a study to identify ecotourists’ environmental concern for sustainability in Australia. For this, 243 respondents having ecotourism experience were surveyed based on the nature and learning criteria of previous segmentation studies. Pro-environmental attitudes were measured as an indication of their sustainability.
\nZhuang et al. [47] conducted a study in Laojunshan area of Northwestern Yunnan Province in Western China to examine an ecotourism demonstration project. They analyzed the strengths and weaknesses of the primary actors such as government, private business, NGO, and local people to evaluate the potential for the development of ecotourism in the current institutional environment in China. In the first stage, government documents and GEI reports from the Laojunshan Ecotourism Project were reviewed to develop a guide for semi-structured interviews. Key stakeholders from different institutions were identified in collaboration with GEI staff. Field interviews were conducted by the first author using a fairly open framework for focused conversations with a core set of questions. Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis and an analytical hierarchy process (AHP) were used to analyze the data.
\nK.C. et al. [5] conducted a study in the Ghandruk Village Development Committee of Annapurna Conservation Area, Nepal to assess the impacts of ecotourism on environmental conservation, social and cultural heritage preservation, economic development and enhancement of livelihoods. Two hundred and forty two households were interviewed, followed by three focus group discussions and five key informant interviews.
\nObua [48] conducted a study to assess the environmental impacts of recreation on camping sites and nature trails in Kibale National Park in Uganda. Nine physical parameters were assessed in camping sites and four parameters on nature trails of the national park.
\nKelkit et al. [9] conducted a study in Kazdagi (Mt. Ida) National Park, Turkey to assess tourism activities sensitive to the environment and spread ecotourism by protecting biological species in Turkey and neighboring countries. Strength, Weakness, Opportunity and Threat analysis of the Park was done to derive strategic proposals for future development.
\nWinson [49] conducted an empirical case study to examine efforts on the Island of Cuba to establish environmentally sensitive and sustainable tourism. The study was conducted with face-to-face interviews with key informants from semi-autonomous agencies organizing eco-tours, personnel at government ministries, management personnel involved in ecotourism and government park managers involved with tourism operations.
\nBallantyne and Pickering [50] conducted a scoping assessment to identify the loss of orchids by tourism activity in the wild by directly collecting, habitat clearance and trampling and indirectly by weeds, pathogens and climate change using data on Australian threatened orchids.
\nCoghlan [51] assessed the relationships between natural resource management and tour operators’ inputs and outputs with the help of quantitative and secondary data collection methods. Input data were collected from a variety of secondary sources whereas output data were collected through 4800 surveys of reef visitors from November 2006 to December 2008. Analysis of data was carried in SPSS 16.
\nMaikhuri et al. [52] conducted a study in Nanda Devi Biosphere Reserve, India with the help of detailed household survey and interviews with buffer zone villages to collect information on socio-economic conditions and population structure.
\nClifton and Benson [53] conducted a study to identify the nature and causes of socio-cultural impacts of ecotourism from Indonesia with the help of semi structured interviews with local residents and ecotourists.
\nDicken [54] conducted a study in Pondoland Marine Protected Area, South Africa to assess the recreational aspects of the boat-based tourism industry focused on the sardine run and estimate the economic value to the local communities with the help of face to face questionnaire survey.
\nGezon [55] conducted a study in Ankarana protected area, Madagascar using a qualitative and longitudinal approach focusing on the social impacts of tourist intensification by comparing and contrasting communities nearby the protected area.
\nBrunet et al. [19] conducted a study in Bhutan to assess the relationship of tourism, development, culture and environment. Primary data was collected through field visit, participant observation and interviews with stakeholders while secondary data was collected from electronic and printed texts.
\nMaroudas and Kyriakaki [20] conducted a study in two small islands of Dodecanese to describe and assess the relations and interactions between local development and ecotourism.
\nBoxill and Severin [56] conducted an exploratory study on tourism development and its impact on the caribs of Dominica.
\nSilva and McDill [57] conducted a study in Pennsylvania and Maryland in the United States to compare agency and business perspectives on barriers affecting ecotourism suppliers. Data were collected through forty five face to face key informant interviews with business owners, tour operators and outfitters, state and local government personnel, natural resource managers working on public lands and conservation leaders.
\nNath and Alauddin [24] conducted a study in Sitakunda Botanical Garden and Eco-park, Chittagong, Bangladesh to assess the impact of park in rural community. Socio-economic survey, personal observations, informal discussion and key informant interviews was conducted using a semi-structured questionnaire with visitor and park managers.
\nDuffy [4] conducted a study in Madagascar to observe the development of ecotourism in the wider debates of neoliberalism and commodification of nature. Thirty six semi-structured interviews with key interest groups involved with environmental policymaking were conducted during a two months of fieldwork in Madagascar in 2004.
\nLacher and Nepal [58] conducted a study to document and examine local-level strategies employed to reduce leakages of tourism revenue in three peripheral regions of Northern Thailand. The study was conducted between October and December 2006 by conducting informal interviews with tourism entrepreneurs, local tourist guides, village officials and village headman.
\nZambrano et al. [59] conducted a study in Osa Peninsula of Costa Rica to assess social and environmental effects of ecotourism. The study was conducted with the help of socioeconomic data from owners, operators, managers, team leaders and locals gathered during the field work from June–August 2005.
\nGallagher and Hammerschlag [60] conducted a study to examine the distribution, frequency and economic value of shark based ecotourism. In 83 locations of 8 geographic regions, 376 shark ecotour operations were identified. A socioeconomic case study of shark tourism in South Africa was conducted from October to November 2010 by taking information on customer served and cost per trip. Consumptive and non-consumptive values of shark resources was compared and discussed. The relative economic importance of shark-based tourism at global scale and potential implications of the industry was evaluated.
\nAmati [30] conducted a study in Kimana Community Wildlife Sanctuary, Kenya to explore individual and household experiences of long-term participation with the help of in-depth interviews, a survey, participant observation and secondary data.
\nWood et al. [61] evaluated sustainability of behavioral changes associated with the agreements and development activities supported by the grants and identified determinants of success and lessons in around Kerinci Seblat National Park in Indonesia. They conducted focus-group discussions with village leaders, farmers and others involved in the ICDP and semi structured interviews with villagers, park staffs and local NGO representatives and District Head’s office in three Districts.
\nAcharya and Halpenny [38] conducted a study in Barpak, Gorkha, Nepal, to assess the role of homestay tourism in sustainable community development. The study was analyzed through community-based action research and evaluation methodologies. The primary information was generated through ethnographic observations and semi-structured interviews with politicians, social workers, officials, members of youth clubs, NGO workers and women from the homestay households.
\nK.C. and Thapa Parajuli [36] conducted a study in Manaslu conservation area (MCA) in Gorkha district of Nepal to assess the impact of ecotourism on livelihood of local people. Seventy-six household surveys, three focus group discussions and five key informant interviews were conducted to get the primary information. Graphical, correlation and regression analysis was carried out for analysis of collected information.
\nStem et al. [6] conducted a study in Costa Rica to assess ecotourism benefits, impacts and its potential for promoting conservation and community development with the help of semi-structured individual and group interviews and direct observation.
\nPuhakka and Siikamaki [62] examines nature tourists’ environmental values and perceptions of ecolabels in PAN Parks certified Oulanka National Park in northeastern Finland. The method includes 273 surveys, 212 to Finnish tourists and 61 questionnaires by foreign tourists of 13 European and 3 other countries.
\nCheung and Jim [29] conducted a study in four remote ecotourism hot spots in Hong Kong to understand the resource base and visitor attitude and expectation. The study was conducted with the help of 456 questionnaire surveys with visitors to evaluate the preferences of ecotourism services.
\nWith an objective to identify the local tourism resources and calculate the ecotourism carrying capacity, Shi et al. [27] conducted a study in Shangri-La County in China. The information related to ecotourism related aspects and a resource was gathered with the help of seminars, randomized surveys and consultations with local authorities between September and October 2011.
\nNepal [63] conducted a study to find the areas of satisfaction and concerns that tourists expressed on their stay in the lodges of Annapurna Conservation Area and its effect on their total ecotourism experience. The study was conducted in May 2004 for a period of three weeks based on on-site exit interviews with 130 trekkers in the Annapurna region by in-depth qualitative discussions.
\nBaral et al. [39] conducted a study in Annapurna Conservation Area (ACA), Nepal to evaluate ecotourism and rate their level of satisfaction from their ecotourism experience. For this, a sample of 315 international visitors were surveyed in April and May of 2006 in Ghorepani. Statistically valid and reliable confirmatory factor analysis ecotourism evaluation scale containing seven items was applied to assess visitors’ perceptions of the socio-economic and environmental outcomes of ecotourism.
\nBlack et al. [64] conducted a study on range of Ecotour guide training in less developed countries with the help of literature review and emailed questionnaire survey to 240 individuals. The research was based on training models, program aims, structure and content of existing training, trainers and trainees assessment methods and program evaluation.
\nOrmsby and Mannle [65] conducted a study in Masoala National Park, Madagascar focusing on ecotourism benefits and the role of local guides in promoting conservation awareness with the help of interviews, participant observation, and archival research to park’s guide association and residents.
\nSerenari et al. [66] conducted a study in Garhwal, Uttarakhand from May-June, 2009 with a goal to predict guides’ intentions on environmentally significant behaviors on their expeditions in Garhwal. The study was conducted with the help of 68 questionnaires by snowball sampling to guides and visiting adventure tour companies, guide agencies and storefronts.
\nMatysek and Kriwoken [67] conducted a study to assess the nature and ecotourism program in Tasmania with the help of in-depth structured interviews with a key informant group of operators, industry representatives, government officials and academics. A qualitative research was conducted on an individual and personalized basis.
\nCatibog-Sinha and Wen, [28] conducted a study in Xishuangbanna Biosphere Reserve (XBR), South China to assess the importance of effective tourism planning and management through the integration of the social, economic and environmental goals. The study focuses on the planning stage of the 7Es model, Environment, Economics, Enforcement, Experience, Engagement, Enquiry and Education.
\nChung [68] conducted a study in Hong Kong to assess the problem of implementing international ecotourism certification at local level with the help of face-to-face interviews, tele-interviews and mailed-questionnaires. The views of stakeholders on international certification for ecotourism in Hong Kong were diversified.
\nDeng et al. [69] conducted a study in West Virginia to create a point evaluation system to rate and rank forest-based ecotourism areas in West Virginia, USA based on input from academics and ecotourism operators using the Delphi method. It involved one hundred ecotourism academics and sixty one ecotourism operators. Twelve criteria having several indicators were pre-identified from the literature while criteria and associated indicators in the second round were redesigned to reflect the destination conditions and the destination management.
\nDeng and Selin [70] conducted a study for the development of a point evaluation system for ecotourism destinations. It involves first round of panel discussion for generation of ideas and the second round for ranking and scoring of measurement items.
\nDifferent issues of ecotourism were raised by different researchers throughout the world. Issues such as challenges to ecotourism, impacts of ecotourism focusing on environmental aspects, social aspects and economic aspects, visitors attitude towards ecotourism, role of local guide in ecotourism and ecotourism certification were raised by the researchers. Primary and secondary sources of information were analyzed by researchers through literature reviews, questionnaire survey, semi structured interviews, key informant interviews, participant observation, Strengths, Weaknesses, Opportunities, and Threats’ (SWOT) analysis and qualitative researches. This chapter identified location, objectives and methodologies implemented in the ecotourism researches. Methodologies implemented by different researchers were quiet similar but the objectives and study area were different.
\nInfrastructures for tourism have given many benefits for local residents [14]. The ecotourism industry is taken as the driving force to accelerate the socio-economic development, change the traditional growth patterns and construct a harmonious and environment-friendly society [71]. Ecotourism and its impacts on environmental aspects, social aspects and economic aspects throughout the world are described briefly in this part of the chapter with the help of literature review of the researches on impacts of ecotourism.
\nEcotourism is supply led tourism strategy that enables sustainable development with the central theme of ecosystem integrity. It is dictated by the ability of the ecosystem to absorb the impacts but does not exclude the tourist and commercial exploitation of the ecosystem. Ecotourism does not set limits on tourist numbers from the notion of carrying capacity but only controls the nature and extent of their activity [16]. Atmospheric quality, sound quality, drinking water, sewage, lampblack and all kinds of establishments should meet the relative ordains of the national standards of environmental conservation [14].
\nIt has significant potential to generate direct community benefits from conservation. It protects the environment, saves unique species and helps to earn money from tourists [65]. Parks helps in combining conservation and development through a program to bring tourism benefits to local committees [65]. Commercial hiking and trekking encourage development where guest facilities and businesses are located [41]. Local people realize the importance of conservation and protect the environment actively [14]. Conservation of nature attracts tourist and provide high level of satisfaction by exploring the nature and provide partnerships between protected area managers and tourism operators [51]. Forest coverage rate can be increased which provide solid foundation for developing ecotourism [71]. Ecotourism emphasized the protection of natural resources and biological diversity during the tourism development. It also requires maintaining the sustainability of resource use and transforming the advantages of regional tourism resources into industrial and economic advantages [71].
\nEcotourism positively influence conservation near protected areas and decreases deforestation and hunting rate. Alternative ecotourism provide economic benefits and discourage the conversion of forest to agricultural and pastoral land. Resource management and financial components play a great role in conservation. Its attractiveness rests in its potential to provide local economic benefits while also maintaining ecological resource integrity through low-impact and non-consumptive use of local resources. Ecotourism has pushed its way to the forefront as one of the preferred tools for conservation and community development in many rural areas [6].
\nEcotourism is a strategy that has been used to create sustainable economic development, while pursuing conservation objectives. It is a tool that may balance the conflicting goals of economic development and biodiversity conservation [2]. It is developed for the benefit of communities and for conservation activities [4]. In ecotourism, natural resources are not threatened and local people are encouraged in park protection. Rich natural resources are protected together with the recreational and tourist potential which creates a center of attraction [9]. Ecotourism project enriches the area with natural regeneration, new plantations, infrastructure development and new destination for visitors and nature-based tourism [24].
\nEcotourism has a positive impact on education, job training, hunting, and deforestation [59]. There is also positive benefit to flora and fauna [49]. It is able to limit environmental and cultural impact of Western countries [19]. By encouraging both ecological sustainability and grassroots development, community-based ecotourism hope that the environmental sensitivity and responsibility promoted by ecotourism can serve the political, economic, social and environmental interests of host communities [21]. Ecotourism supplies real experiences of natural environments [16]. Environmentally and culturally sensitive ecotourism programs can play a key role in justifying the rationale for the existence, maintenance and future benefits of protected areas worldwide. They may also serve to mediate conflict by explaining to local residents the purpose of conservation and the roles of park personnel, and can be a means of strengthening park staff’s community visibility and credibility [65]. It is an excellent solution to increased tourists and increased degradation to natural areas [72].
\nConcentration of ecotourism services within a region can marginalize neighboring human communities without causing environmental degradation. Multi-sector planning efforts can maximize profits, distribute benefits equitably and minimize adverse environmental impacts as tourist numbers increase [55]. The environment-friendly characteristic of ecotourism not only brings more economic benefits but also has notable ecological benefits [71].
\nTourism is an industrial activity that exerts a series of impacts that are similar to most other industrial activities. It consumes scarce resources, produces waste by-products and requires specific infrastructure and superstructure to support it [73]. Ineffective management of tourism causes negative impact on protected area resources [18]. It provides an alternative mechanism of resource utilization which may bring adverse impact on environmental resources. Litter problems had occurred in 44% of the nature reserves, water pollution in 12%, noise pollution in 11% and air pollution in 3% of the nature reserves in China [25]. Camping sites felt environmental degradation in both wet and dry seasons but more in dry season. Environmental degradation is a common problem which increases with increase in visitor number in a protected area open to recreational use [48].
\nCamping and caravanning zones will add increased levels of pollution to the nearby river [9]. The ecosystem may become less stable and local pollution may have drastic effects, such as fish kills [16]. Ecotourism will require more space for tourists and increased clearing of land [72]. Farmers’ extensive farming methods can bring some negative effects, such as sharp drop of forest, vegetation damage, desertification and soil erosion [71]. Successful ecotourism initiatives may draw increasing interest and a correspondingly higher number of tourists, thus intensifying negative impacts such as solid waste generation, habitat disturbance, and trail erosion. Such impacts could seriously threaten the resources upon which ecotourism depends [6].
\nThe social impacts of tourism are minimal but are largely positive [56]. The growth of tourism led to migration of people to the area [41]. Guest houses provide food and beverages and are located along the main access route to the National Park [41]. Economic benefits of tourism are successful to stop political opposition to economically unviable and environmentally harmful tourism projects [23].
\nThe local residents show traditional hospitality and their honest folkway in spite of the modernization. The traditional culture has been strengthened in the course of economic development [14]. Ecotourism operators may organize cultural tours with different community groups to ensure experience of both the tourists and community members [6]. The rich culture of the neighborhood can be introduced to visitors [9]. For sustainable ecotourism, lodges should behave as good neighbors to the local people and visitors [6]. Tourism is conducive to the social stability and harmony [71].
\nEcotourism offers diverse products and services that can be categorized as both a service and an experience. Ecotourism sites, such as historic houses or theme parks, are primarily consumed for their experiential/ emotional dimensions, which is more a representation of consumption of service experiences rather than relating to their functional properties. Thus, the psychological benefits gained by ecotourists are deemed more relevant and important in this context [74]. There is no conflict between development of planting and conservation of environment. The development of rural tourism provides the villagers with second revenue [14].
\nTourism intervention has the potential to promote social conflict and challenge cultural values [15]. It may results in conflicts between the locals and government authorities [52]. There is increase in alcoholism, drug addiction and prostitution as a result of tourism [59]. Foreign cultures brought by tourists have an impact on local traditional and plain cultures [71]. Due to tourism, traditional local people having simple life but material needs are vulnerable to Western influence [28]. Copy of Westerners clothing by wearing vests by young people is intolerable in some place [53]. Younger generation conduct between sexes and consumption of alcohol is inappropriate to local norms [53]. There is conflicting experiences of participation in conceptualization and operation of their communal ecotourism initiative. Community members do not share same understanding and appreciation of financial growth [30].
\nEcotourism enhances conservation and development by providing environmental and economic benefits [6]. There was socio-cultural change from subsistence to market economy and changes in traditional land resource rights and institutions [52]. Economic benefit in ecotourism comes from direct employment, rental of accommodation for visitors and the sale of handicrafts and food [53]. Ecotourism provides local economic benefits such as employment, improved infrastructure, increased business for local stores and also maintain ecological resource integrity through low-impact, non-consumptive resource use [6].
Rural tourism provides peasantry with more profit, more employment and better living conditions by promoting the linkage of agriculture and tourism [14]. It develops rural economy and rural culture. It brings a lot of economic income for the local villagers and provides second revenue [14]. It can attract developed region’s funds to invest in periphery regions through construction, bring revenue through flows of passengers, transportation, and accommodation and solves the poverty problem. It helps to transfer the rural surplus labors to non-agricultural industries, change the traditional rural lifestyle and productive style, increase the income of farmers and improve the welfare [71]. It had great contribution to the regional economy [71].
The environment-friendly characteristic of ecotourism brings economic and ecological benefits [71]. Population having experience and skill for highland tourism business takes ecotourism as the most desirable and important economic opportunity. The major sources of revenue generation in tourism are restaurants, souvenirs shops, guiding services, accommodation and entrance fees [58]. Additional sources of income from ecotourism might include sale of traditional handicrafts or agricultural products [2]. Traditional local handicrafts can be sold to tourist to increases economic gains of local communities [9]. Communities try to enhance economic importance and wanted to develop a greater involvement in the future [54].
Many parks works as government agency to collect revenue from fee and license, retention of budget surpluses, set prices to overcome the cost of production and coordinate with corporate and non-profit entities [18]. Ecotourism financially supports protected areas through tourism-related park fees [6]. It provides revenue to the park authority and income to local people [24]. Ecotourism is already the largest source of foreign exchange in many countries [1].
Ecotourism generate foreign exchange earnings, employment, and other economic and social benefits in the areas so it helps in developing thoughtful economic policy and institutional development [18,23]. Accommodation industry are provided more subsidies, tax cuts, concessions, promotional packages and soaps to contribute for the growth and development of the economy [23]. Ecotourism can be an integral part of the master development strategy of a developing nation [75].
Half of park visitor entrance fees are allocated to local management committees for development projects of their choice such as road improvements, construction of tables for a primary school, well and public toilet construction and building rehabilitation [65]. Due to the increase in tourist number, infrastructure for tourism and tour guiding have been developed [55]. Tourism can have both environmental and social consequences [55].
Other than the cultural centre and resource access, tourism creates jobs for community members, helps in socio-economic development and generates revenue from conservation [44]. Tourism is an important source of employment for the people in the territory [56]. Economic benefits for local residents rates the direct employment of locals in the tourism industry, including hotels, lodges, tourist restaurants, and tourist chauffeuring [75]. It should be taken as the important way to increase employment and release environmental pressure [71]. It helps to transfer the rural surplus labors to non-agricultural industries, change the traditional rural lifestyle and productive style, increase the income of farmers and improve the welfare [71]. It provides more native employment in lower job status and income [1,41,59]. Women make up to 45.3% of all staff working in guest houses and restaurants. Nearly 80% of the helping staff is family members with 50% women helpers. But most of the tourism businesses are owned and operated by male migrants or old established noble families [41]. It is labor intensive and offers a variety of small scale opportunities creating jobs for poor, women, young people and indigenous community [1]. Tourism and recreation inside the park provides employment and new business opportunities [9]. The guides working in the tour companies come from local villages and other settlements and know the environment well [9]. In some cases, community involvement is rudimentary and limited to temporary employment (3–4 weeks) in the form of maids, cooks, entertainers, porters and security guards [54].
Ecotourism can bring negative social, cultural and economic impacts. In addition to its potential environmental drawbacks, it often fails to provide widespread economic benefits [6]. There is major impact on the local economy due to ban on trade and mountaineering/expeditions and trekking activities [52]. Ineffective management of tourism causes negative impact on park resources [18].
From the above discussion, environmental, social and economic benefits of ecotourism were observed. Ecotourism has helped in environmental conservation, natural regeneration, new plantations, increase in forest cover, protection of natural resources and biological diversity, conservation of flora and fauna and decrease in deforestation and hunting rate. In spite of these positive environmental impacts, it consumes scarce resources, produces waste by-products, requires specific infrastructure and superstructure, causes litter problem, air pollution, water pollution, noise pollution, destruction of fish, increased clearing of land, drop of forest, vegetation damage, desertification, soil erosion, habitat disturbance and trail erosion.
Increase in traditional hospitality and honest folkway, social stability and harmony, development of rural culture are positive social aspects while negative aspects includes promoting social conflict by challenging cultural values, increase in conflicts between the locals and government authorities, increase in alcoholism, drug addiction and prostitution, adverse impact on local traditional and plain cultures and also makes simple life vulnerable to Western influence.
Economic benefit in ecotourism comes from direct employment, rental of accommodation for visitors, sale of handicrafts and food, improved infrastructure, increased business for local stores, better living conditions, promotion of linkage between agriculture and tourism, development of rural economy, transfer of rural surplus labors to non-agricultural industries, change in traditional rural lifestyle and productive style and increase in income of farmers. Negative impact on the local economy is due to ban on trade, mountaineering/expeditions and trekking activities and negative impact on park resources.
\nEcotourism helps in sustainable development of the country. As, Nepal is rich in biological, cultural and social diversity, there is a great scope of sustainable development from ecotourism. By observing these benefits, this part assesses the impacts of ecotourism in environment, society and economy with the help of researches conducted in Nepal.
\nEcotourism helps in natural resource management and biodiversity conservation in Ghandruk. Increase in forest cover, conservation of flora and fauna, increase in greenery and use of alternative energy sources are the positive environmental impacts of ecotourism. Floral and faunal diversity had also been increased [5].
In ACA, there are clean and comfortable stays, good local cuisine, outstanding natural scenery and positive interactions with host communities. Satisfaction rating of living room comfort, overall cleanliness, views, peaceful atmosphere, host friendliness, price, and local food exceeded the importance attached to them while bedroom options, toilet, shower, menu, Western food, lodging recommendations and environmental quality did not exceed the importance rating. But the satisfaction of environmental quality was rated very high [63].
Ecotourism had helped in increasing mutual help and cooperation, controlling antisocial activities and conserving religious and cultural heritage in Ghandruk. It had also played a great role in maintaining peace and prosperity in the society. There is increase in cooperation of people with religious beliefs and religious tolerance [5].
The local people felt that the presence of visitors in remote area enhanced their pride in Barpak. Increased excitement of children was the positive outcomes of ecotourism. Success of ecotourism is due to coordination of local community members, line agencies and various levels of government, transparency in the development of the homestay progras, clear guidelines and standards, hospitality training and monitoring of environment and bottom up approach of women taking local level ownership and control. Also, ecotourism entrepreneurs sit in a meeting every month reviews the activities and takes feedback to improve their services. Ecotourism addresses gender inequities by the active involvement of women in economic activities associated with tourism [38].
Government of Nepal recognizes tourism as a priority sector and major contributor to Nepal’s economy. The total foreign exchange earnings from tourism in Nepal in fiscal year 2010/2011, 2011/2012 and 2012/2013 was Rs. 24,611.0, 30,703.8 and 34,210.6 million, respectively. The tourism sector contribution in GDP was 1.5, 1.4, 2.3, 2.8, 2.4, 1.8, 2.0 and 2.0 percent in 2005/2006, 2006/2007, 2007/2008, 2008/2009, 2009/2010, 2010/2011, 2011/2012 and 2012/2013, respectively. The highest total foreign exchange earnings from tourism were Rs. 34,210.6 million in 2012/2013 [40].
Ecotourism is one of the meaningful sources of economic development and job creation in Ghandruk. It had encouraged people to establish hotels, restaurants, tea shops, bakery cafes, grocery and gift shops. It has brought a lot of economic income for the local villagers [5]. Ecotourism provides fixed price of available services in a package as per the choice of the tourist in the format of a menu in Barpak [38].
Ecotourism participation had increased household consumption in MCA. By participating in ecotourism activities, people are able to generate more income and are increasing their household consumption [36].
Ecotourism has been widely recognized for its role in employment generation and contribution to the national economy. Trekking is recognized as a major part of this industry in Nepal. It can generate jobs directly through hotels, restaurants, taxis, souvenir sales, local guides and indirectly through the supply of goods and services needed by tourism related business. The impressive growth of Pokhara is a good example of how tourism can contribute to economic growth. Similarly, settlements along the trekking routes in ACA have received tourism benefits, which have contributed to development of the region [76].
Ecotourism has immense potential to help in poverty alleviation in Nepal. It had made significant contribution to rural development, agricultural transformation, community enrichment and social empowerment of women in ACA. Because of higher incomes, many parents of Manang District of ACA can now afford to send their children to high schools and universities in Kathmandu [77].
From the above findings, it is observed that ecotourism helps in natural resource management, biodiversity conservation, increase in forest cover, conservation of flora and fauna and increase in greenery and use of alternative energy sources in Nepal. It develops mutual help and cooperation, controls antisocial activities, conserves religious and cultural heritage, maintains peace and prosperity in the society and increases cooperation of people with religious beliefs and religious tolerance. Tourism is a major contributor to Nepal’s economy and helps in economic development, job creation, increase in household consumption and poverty alleviation. Also, it had made significant contribution to rural development, agricultural transformation, community enrichment and social empowerment.
\nSelf-reliant communities have greater chance of progress in ecotourism [78]. There is need for sustainable use of tourism resources, proper handling of tourism demands, repartitioning tourism flow and digital system for tourism management [27]. Expertise in park management and finance management lowers negative environmental impacts and increases positive economic impact. There will be major shift in park management, tourism management and financial management which will help in sustainable development of tourism [18].
Local income, biodiversity conservation and cultural preservation are necessary for tourism intervention in remote areas. There is need of improvement of accessibility, liberalization of pricing policy, community empowerment, and development of tourism facilities and incorporation of nature-based tourism products. To generate social, economic and environmental benefits, tourism intervention in protected areas must make a multi or interdisciplinary and interdepartmental effort. This requires strengthening collaboration between the conservation and tourism authorities to develop ecotourism inside the park [15]. Cultural tourism need to be developed to preserve cultural traditions and enrich the quality of the life of local communities. It is necessary to develop and promote minority cultures to support preserving cultural traditions and education and training programs of ecotourism and nature reserve for tourism operators, employees and tourist [17].
Ecotourism requires four tradeoffs: success and survival at the expense of ecotourism’s spatial isolation and structural independence; local employment and benefits at the expense of local initiation and control; social status and mobility at the expense of social cohesion and harmony; and incipient environmentalism at the expense of ecological sustainability [21]. Successful ecotourism ventures depend on an integrated management philosophy that considers beneficiaries, socioeconomic constraints and impacts on the resource base within a realistic spatial and temporal scale. It requires that entire ecosystems, or at least the biological diversity that they contain, are managed in relation to the tourism experience [16]. Ecotourism and its sustainability require the inclusion of local participation/benefits, economic viability, education dissemination, tourist satisfaction and the minimizing of tourism impact [32].
Parks and protected areas need people’s effective participation for meaningful resource management [78]. If locals are actively involved in tourism planning and development, it becomes much easier to get their support for conservation and avoid serious conflicts with the management [22]. Support and participation of local communities is important for development and management of the nature reserve [17]. Community participation is needed to make ecotourism sustainable [56]. Community-based programs such as community organizing, environmental education and leadership training should be done with the involvement of relevant stakeholders in a collective manner [28].
After privatization of tourism facilities and services, park management encouraged the local communities to involve directly in delivering tourism activities. Nature guiding training is provided to local communities to enhance themselves in this sector. Local tourism provider associations are established to secure local interest in tourism participation [79]. Local economic diversity is also important to the sustainability of community-based ecotourism projects [2]. Mechanisms for managing conflicts between protection and development faced by the nature reserves should be clearly addressed [25].
Establishment of the National Network of Protected Areas needs sound management and should ensure long-term maintenance and achieve the goal of promoting local economic development within the communities [22]. The management committee should be formed which include all government agencies responsible in natural resources management and economic development. There is need of cooperation between local governments, local communities, NGOs and the private sector [17]. Development of park management framework for staff and finance management play a key role in running a park effectively [18].
Local government officials had a great role for conservation, development and logistical functions towards tourism development [17]. Local governments should develop fund from provincial and national governments or NGOs for managing the reserve [17]. Appropriate measures to protect the core area from over-exploitation and unsustainable human use need to be conducted by government [28]. Macro-scale institutional organization and coordination, both at the national and international levels, are important for the sustainability of community-based projects. Multiple government departments, including ministries of tourism, natural resources, and rural development, should coordinate policies and programs in order to pursue the success of ecotourism projects. International organizations, which often fund ecotourism projects, must also coordinate with government agencies and local non-profits [2]. Planning and management need to be supervised by a council, advisory board, association, or program, which would be a not-for-profit NGO. The roles of the various stakeholders involved in the project, as well as the hierarchy of the people involved, will need to be clearly articulated, since the success of the ecotourism network will depend on appropriate levels of leadership, cooperation and coordination amongst the involved parties [80].
Conservation agreements effectiveness depends on appropriate role of local circumstance. An agreement between community and park management is beneficial when people are encouraged to abandon behavior that brings conflicts with conservation. Conditional link between the obligations of the community and benefits is created by agreement with external monitoring and enforcement. If the livelihoods are directly dependent on legal and sustainable use of park resource and services, an agreement for internal control is relevant to regulate access to benefits and prevent selfish behavior [61].
Tourism agencies need to enhance their skill and capacity to address climate change. Development of strong leadership, commitment and sufficient resource is necessary to cope with impact of climate change on tourism sector [43]. Knowledge and experience of local community can improve planning and decision making of tourism, conservation and economic development. For the smooth operation of tourism in protected areas, it is important to have trained staff, more efficient administration and better coordination with the contributions of various stakeholders [28]. Hence, trainings on skills, knowledge and quality development is necessary. For appropriate training approach, research on evaluation of formal training and follow up training is necessary. Systematic evaluation of existing informal and formal training is needed to assist in developing new programs and improve existing ones [64].
Positive attitude of guide is important to show pro-environmental behavior while guiding [66]. Visitors prefer good quality information; tour guides and low impact activities and select the destination with high ecological value and good site management [29]. To promote conservation, education programs should be developed for local residents related to nature reserve [17]. Overall planning for ecotourism development is necessary in most nature reserves [25]. There is need of competent staff as tourist guides with good management experience [25].
\nIt was recommended to develop environmental friendly policies focusing on research to understand the motivations and expectations of qualified co-workers for a successful recruitment process and conduction of longitudinal studies to investigate the lifecycle of the individual firms [42]. Management strategies should focus on ways to achieve sustainability by involving local people [54]. Strategic planning, financial analysis, and business plan development are needed to improve the quality and viability of ecotourism [13]. Also, climate change and tourism sector requires a strong policy framework led by a government agency to convert private into public and global flow into local flow in the current time [43].
The main challenge for adoption of natural sciences knowledge in wildlife tourism was lack of competent and dedicated human capital and difficulties in networking. Differences in gender, culture and professional background and difficulties in local networking act as barriers to cooperation within the local context [42]. Lack of education and English language skills is a major obstacle for tourism business development and direct employment [41,53]. The main challenge is to increase the length of stay of visitors which could be done by hiking and camel safaris [44].
\nEcotourism has environmental, social and economic impacts to the community, nation and the whole world. For assessing its issues and impacts, social tools such as questionnaire surveys, focus group discussions, key informant interviews and review of literature were applied by different researchers. It has helped in sustainable development of Nepal and the whole world by conserving environment, increasing employment, enhancing livelihood and promoting the culture and traditions. There is need of cooperation among different stakeholders, training of ecotourism to tourism entrepreneurs and appropriate management policy for sustainable implementation of ecotourism projects.
\nIn recent years, researchers in the software engineering (SE) field have turned their interest to data mining (DM) and machine learning (ML)-based studies since collected SE data can be helpful in obtaining new and significant information. Software engineering presents many subjects for research, and data mining can give further insight to support decision-making related to these subjects.
Figure 1 shows the intersection of three main areas: data mining, software engineering, and statistics/math. A large amount of data is collected from organizations during software development and maintenance activities, such as requirement specifications, design diagrams, source codes, bug reports, program versions, and so on. Data mining enables the discovery of useful knowledge and hidden patterns from SE data. Math provides the elementary functions, and statistics determines probability, relationships, and correlation within collected data. Data science, in the center of the diagram, covers different disciplines such as DM, SE, and statistics.
The intersection of data mining and software engineering with other areas of the field.
This study presents a comprehensive literature review of existing research and offers an overview of how to approach SE problems using different mining techniques. Up to now, review studies either introduce SE data descriptions [1], explain tools and techniques mostly used by researchers for SE data analysis [2], discuss the role of software engineers [3], or focus only on a specific problem in SE such as defect prediction [4], design pattern [5], or effort estimation [6]. Some existing review articles having the same target [7] are former, and some of them are not comprehensive. In contrast to the previous studies, this article provides a systematic review of several SE tasks, gives a comprehensive list of available studies in the field, clearly states the advantages of mining SE data, and answers “how” and “why” questions in the research area.
The novelties and main contributions of this review paper are fivefold.
First, it provides a general overview of several SE tasks that have been the focus of studies using DM and ML, namely, defect prediction, effort estimation, vulnerability analysis, refactoring, and design pattern mining.
Second, it comprehensively discusses existing data mining solutions in software engineering according to various aspects, including methods (clustering, classification, association rule mining, etc.), algorithms (k-nearest neighbor (KNN), neural network (NN), etc.), and performance metrics (accuracy, mean absolute error, etc.).
Third, it points to several significant research questions that are unanswered in the recent literature as a whole or the answers to which have changed with the technological developments in the field.
Fourth, some statistics related to the studies between the years of 2010 and 2019 are given from different perspectives: according to their subjects and according to their methods.
Five, it focuses on different machine learning types: supervised and unsupervised learning, especially on ensemble learning and deep learning.
This paper addresses the following research questions:
RQ1. What kinds of SE problems can ML and DM techniques help to solve?
RQ2. What are the advantages of using DM techniques in SE?
RQ3. Which DM methods and algorithms are commonly used to handle SE tasks?
RQ4. Which performance metrics are generally used to evaluate DM models constructed in SE studies?
RQ5. Which types of machine learning techniques (e.g., ensemble learning, deep learning) are generally preferred for SE problems?
RQ6. Which SE datasets are popular in DM studies?
The remainder of this paper is organized as follows. Section 2 explains the knowledge discovery process that aims to extract interesting, potentially useful, and nontrivial information from software engineering data. Section 3 provides an overview of current work on data mining for software engineering grouped under five tasks: defect prediction, effort estimation, vulnerability analysis, refactoring, and design pattern mining. In addition, some machine learning studies are divided into subgroups, including ensemble learning- and deep learning-based studies. Section 4 gives statistical information about the number of highly validated research conducted in the last decade. Related works considered as fundamental by journals with a highly positive reputation are listed, and the specific methods they used and their categories and purposes are clearly expressed. In addition, widely used datasets related to SE are given. Finally, Section 5 offers concluding remarks and suggests future scientific and practical efforts that might improve the efficiency of SE actions.
This section basically explains the consecutive critical steps that should be followed to discover beneficial knowledge from software engineering data. It outlines the order of necessary operations in this process and explains how related data flows among them.
Software development life cycle (SDLC) describes a process to improve the quality of a product in project management. The main phases of SDCL are planning, requirement analysis, designing, coding, testing, and maintenance of a project. In every phase of software development, some software problems (e.g., software bugs, security, or design problems) may occur. Correcting these problems in the early phases leads to more accurate and timely delivery of the project. Therefore, software engineers broadly apply data mining techniques for different SE tasks to solve SE problems and to enhance programming efficiency and quality.
Figure 2 presents the data mining and knowledge discovery process of SE tasks including data collection, data preprocessing, data mining, and evaluation. In the data collection phase, data are obtained from software projects such as bug reports, historical data, version control data, and mailing lists that include various information about the project’s versions, status, or improvement. In the data preprocessing phase, the data are preprocessed after collection by using different methods such as feature selection (dimensionality reduction), feature extraction, missing data elimination, class imbalance analysis, normalization, discretization, and so on. In the next phase, DM techniques such as classification, clustering, and association rule mining are applied to discover useful patterns and relationships in software engineering data and therefore to solve a software engineering problem such as defected or vulnerable systems, reused patterns, or parts of code changes. Mining and obtaining valuable knowledge from such data prevents errors and allows software engineers to deliver the project on time. Finally, in the evaluation phase, validation techniques are used to assess the data mining results such as k-fold cross validation for classification. The commonly used evaluation measures are accuracy, precision, recall, F-score, area under the curve (AUC) for classification, and sum of squared errors (SSE) for clustering.
KDD process for software engineering.
In this review, we examine data mining studies in various SE tasks and evaluate commonly used algorithms and datasets.
A defect means an error, failure, flaw, or bug that causes incorrect or unexpected results in a system [8]. A software system is expected to be without any defects since software quality represents a capacity of the defect-free percentage of the product [9]. However, software projects often do not have enough time or people working on them to extract errors before a product is released. In such a situation, defect prediction methods can help to detect and remove defects in the initial stages of the SDLC and to improve the quality of the software product. In other words, the goal of defect prediction is to produce robust and effective software systems. Hence, software defect prediction (SDP) is an important topic for software engineering because early prediction of software defects could help to reduce development costs and produce more stable software systems.
Various studies have been conducted on defect prediction using different metrics such as code complexity, history-based metrics, object-oriented metrics, and process metrics to construct prediction models [10, 11]. These models can be considered on a cross-project or within-project basis. In within-project defect prediction (WPDP), a model is constructed and applied on the same project [12]. For within-project strategy, a large amount of historical defect data is needed. Hence, in new projects that do not have enough data to train, cross-project strategy may be preferred [13]. Cross-project defect prediction (CPDP) is a method that involves applying a prediction model from one project to another, meaning that models are prepared by utilizing historical data from other projects [14, 15]. Studies in the field of CPDP have increased in recent years [10, 16]. However, there are some deficiencies in comparisons of prior studies since they cannot be replicated because of the difference in utilizing evaluation metrics or preparation way of training data. Therefore, Herbold et al. [16] tried to replicate different CPDP methods previously proposed and find which approach performed best in terms of metrics such as F-score, area under the curve (AUC), and Matthews correlation coefficient (MCC). Results showed that 7- or 8-year approaches may perform better. Another study [17] replicated prior work to demonstrate whether the determination of classification techniques is important. Both noisy and cleaned datasets were used, and the same results were obtained from the two datasets. However, new dataset gave better results for some classification algorithms. For this reason, authors claimed that the selection of classification techniques affects the performance of the model.
Numerous defect prediction studies have been conducted using DM techniques. In the following subsections, we will explain these studies in terms of whether they apply ensemble learning or not. Some defect prediction studies in SE are compared in Table 1. The objective of the studies, the year they were conducted, algorithms, ensemble learning techniques and datasets in the studies, and the type of data mining tasks are shown in this table. The bold entries in Table 1 have better performance than other algorithms in that study.
Ref. | Year | Task | Objective | Algorithms | Ensemble learning | Dataset | Evaluation metrics and results |
---|---|---|---|---|---|---|---|
[18] | 2011 | Classification | Comparative study of various ensemble methods to find the most effective one | NB | Bagging, boosting, RT, | NASA datasets: CM1 JM1 KC1 KC2 KC3 KC4 MC1 MC2 MW1 PC1 PC2 PC3 PC4 PC5 | 10-fold CV, ACC, and AUC |
[19] | 2013 | Classification | Comparative study of class imbalance learning methods and proposed dynamic version of AdaBoost.NC | NB, RUS, RUS-bal, THM, SMB, BNC | RF, SMB, BNC, | NASA and PROMISE repository: MC2, KC2, JM1, KC1, PC4, PC3, CM1, KC3, MW1, PC1 | 10-fold CV Balance, G-mean and AUC, PD, PF |
[20] | 2014 | Classification | Comparative study to deal with imbalanced data | Base Classifiers: C4.5, NB Sampling: ROS, RUS, SMOTE | AdaBoost, Bagging, boosting, RF | NASA datasets: CM1, JM1, KC1, KC2, KC3, MC1, MC2, MW1, PC1, PC2, PC3, PC4, PC5 | 5 × 5 CV, MCC, ROC, results change according to characteristics of datasets |
[17] | 2015 | Clustering/classification | To show that the selection of classification technique has an impact on the performance of software defect prediction models | Statistical: NB, Clustering: KM, EM Rule based: Ripper, Ridor NNs: RBF Nearest neighbor: KNN DTs: J48, | Bagging, AdaBoost, rotation forest, random subspace | NASA: CM1, JM1, KC1, KC3, KC4, MW1, PC1, PC2, PC3, PC4 PROMISE: Ant 1.7, Camel 1.6, Ivy 1.4, Jedit 4, Log4j 1, Lucene 2.4, Poi 3, Tomcat 6, Xalan 2.6, Xerces 1.3 | 10 × 10-fold CV AUC > 0.5 Scott-Knott test α = 0.05, simple logistic, LMT, and RF + base learner outperforms KNN and RBF |
[21] | 2015 | Classification | Average probability ensemble (APE) learning module is proposed by combining feature selection and ensemble learning | RF, GB | NASA: CM1, JM1, KC1, KC3, KC4, MW1, PC1, PC2, PC3, PC4 PROMISE (RQ2): Ant 1.7, Camel 1.6, Ivy 1.4, Jedit 4, Log4j 1, Lucene 2.4, Poi 3, Tomcat 6, Xalan 2.6, Xerces 1.3 | 10 × 10-fold CV, AUC > 0.5 Scott-Knott test α = 0.05, simple logistic, LMT, and RF + base learner outperforms KNN and RBF | |
[22, 23] | 2016 | Classification | Comparative study of 18 ML techniques using OO metrics on six releases of Android operating system | LR, SVM, VP, CART, J48, ADT, Nnge, DTNB | Bagging, random forest, Logistic model trees, | 6 releases of Android app: Android 2.3.2, Android 2.3.7, Android 4.0.4, Android 4.1.2, Android 4.2.2, Android 4.3.1 | 10-fold, inter-release validation AUC for NB, LB, MLP is >0.7 |
[24] | 2016 | Classification | Caret has been applied whether parameter settings can have a large impact on the performance of defect prediction models | NB, KNN, LR, partial least squares, NN, LDA, rule based, DT, SVM | Bagging, boosting | Cleaned NASA JM1, PC5 Proprietary from Prop-1 to Prop-5 Apache Camel 1.2, Xalan 2.5–2.6 Eclipse Platform 2.0–2.1–3.0, Debug 3.4, SWT 3.4, JDT, Mylyn, PDE | Out-of-sample bootstrap validation technique, AUC Caret AUC performance up to 40 percentage points |
[25] | 2017 | Regression | Aim is to validate the source code metrics and identify a suitable set of source code metrics | 5 training algorithms: GD, GDM, GDX, NM, LM | Heterogeneous linear and nonlinear ensemble methods | 56 open-source Java projects from PROMISE Repository | 10-fold CV, t-test, ULR analysis Neural network with Levenberg Marquardt (LM) is the best |
[16] | 2017 | Classification | Replicate 24 CDPD approaches, and compare on 5 different datasets | DT, LR, NB, SVM | LE, RF, BAG-DT, BAG-NB, BOOST-DT, BOOST-NB | 5 available datasets: JURECZKO, NASA MDP, AEEEM, NETGENE, RELINK | Recall, PR, ACC, G-measure, F-score, MCC, AUC |
[26] | 2017 | Classification | Just-in-time defect prediction (TLEL) | NB, SVM, DT, LDA, NN | Bagging, stacking | Bugzilla, Columba, JDT, Platform, Mozilla, and PostgreSQL | 10-fold CV, F-score |
[13] | 2017 | Classification | Adaptive Selection of Classifiers in bug prediction (ASCI) method is proposed. | Base classifiers: LOG (binary logistic regression), NB, RBF, MLP, DT | Voting | Ginger Bread (2.3.2 and 2.3.7), Ice Cream Sandwich (4.0.2 and 4.0.4), and JellyBean (4.1.2, 4.2.2 and 4.3.1) | 10-fold, inter-release validation AUC for NB, LB, MLP is >0.7 |
[27] | 2018 | Classification | MULTI method for JIT-SDP (just in time software defect prediction) | EALR, SL, RBFNet Unsupervised: LT, AGE | Bagging, AdaBoost, Rotation Forest, RS | Bugzilla, Columba, Eclipse JDT, Eclipse Platform, Mozilla, PostgreSQ | CV, timewise-CV, ACC, and POPT MULTI performs significantly better than all the baselines |
[28] | 2007 | Classification | To found pre- and post-release defects for every package and file | LR | — | Eclipse 2.0, 2.1, 3.0 | PR, recall, ACC |
[8] | 2014 | Clustering | Cluster ensemble with PSO for clustering the software modules (fault-prone or not fault-prone) | PSO clustering algorithm | KM-E, KM-M, PSO-E, PSO-M and EM | Nasa MDP, PROMISE | |
[29] | 2015 | Classification | Defect identification by applying DM algorithms | NB, J48, MLP | — | PROMISE, NASA MDP dataset: CM1, JM1, KC1, KC3, MC1, MC2, MW1, PC1, PC2, PC3 | 10-fold CV, ACC, PR, FMLP is the best |
[30] | 2015 | Classification | To show the attributes that predict the defective state of software modules | NB, NN, association rules, DT | Weighted voting rule of the four algorithms | NASA datasets: CM1, JM1, KC1, KC2, PC1 | PR, recall, ACC, F-score NB > NN > DT |
[31] | 2016 | Classification | Authors proposed a model that finds fault-proneness | NB, LR, LivSVM, MLP, SGD, SMO, VP, LR Logit Boost, Decision Stamp, RT, REP Tree | RF | Camel1.6, Tomcat 6.0, Ant 1.7, jEdit4.3, Ivy 2.0, arc, e-learning, berek, forrest 0.8, zuzel, Intercafe, and Nieruchomosci | 10-fold CV, AUC AUC = 0.661 |
[32] | 2016 | Classification | GA to select suitable source code metrics | LR, ELM, SVML, SVMR, SVMP | — | 30 open-source software projects from PROMISE repository from DS1 to DS30 | 5-fold CV, F-score, ACC, pairwise t-test |
[33] | 2016 | — | Weighted least-squares twin support vector machine (WLSTSVM) to find misclassification cost of DP | SVM, NB, RF, LR, KNN, BN, cost-sensitive neural network | — | PROMISE repository: CM1, KC1, PC1, PC3, PC4, MC2, KC2, KC3 | 10-fold CV, PR, recall, F-score, G-mean Wilcoxon signed rank test |
[34] | 2016 | — | A multi-objective naive Bayes learning techniques MONB, MOBNN | NB, LR, DT, MODT, MOLR, MONB | — | Jureczko datasets obtained from PROMISE repository | AUC, Wilcoxon rank test CP MO NB (0.72) produces the highest value |
[35] | 2016 | Classification | A software defect prediction model to find faulty components of a software | Hybrid filter approaches FISHER, MR, ANNIGMA. | — | KC1, KC2, JM1, PC1, PC2, PC3, and PC4 datasets | ACC, ent filters, ACC 90% |
[36] | 2017 | Classification | Propose an hybrid method called TSC-RUS + S | A random undersampling based on two-step cluster (TSC) | Stacking: DT, LR, kNN, NB | NASA MDP: i.e., CM1, KC1, KC3, MC2, MW1, PC1, PC2, PC3, PC4 | 10-fold CV, AUC, (TSC-RUS + S) is the best |
[37] | 2017 | Classification | Analyze five popular ML algorithms for software defect prediction | ANN, PSO, DT, NB, LC | — | Nasa and PROMISE datasets: CM1, JM1, KC1, KC2, PC1, KC1-LC | 10-fold CV ANN < DT |
[38] | 2018 | Classification | Three well-known ML techniques are compared. | NB, DT, ANN | — | Three different datasets DS1, DS2, DS3 | ACC, PR, recall, F, ROC ACC 97% DT > ANN > NB |
[10] | 2018 | Classification | ML algorithms are compared with CODEP | LR, BN, RBF, MLP, alternating decision tree (ADTree), and DT | Max, CODEP, Bagging J48, Bagging NB, Boosting J48, Boosting NB, RF | PROMISE: Ant, Camel, ivy, Jedit, Log4j, Lucene, Poi, Prop, Tomcat, Xalan | F-score, PR, AUC ROC Max performs better than CODEP |
Data mining and machine learning studies on the subject “defect prediction.”
Ensemble learning combines several base learning models to obtain better performance than individual models. These base learners can be acquired with:
Different learning algorithms
Different parameters of the same algorithm
Different training sets
The commonly used ensemble techniques bagging, boosting, and stacking are shown in Figure 3 and briefly explained in this part. Bagging (which stands for bootstrap aggregating) is a kind of parallel ensemble. In this method, each model is built independently, and multiple training datasets are generated from the original dataset through random selection of different feature subsets; thus, it aims to decrease variance. It combines the outputs of each ensemble member by a voting mechanism. Boosting can be described as sequential ensemble. First, the same weights are assigned to data instances; after training, the weight of wrong predictions is increased, and this process is repeated as the ensemble size. Finally, it uses a weighted voting scheme, and in this way, it aims to decrease bias. Stacking is a technique that uses predictions from multiple models via a meta-classifier.
Common ensemble learning methods: (a) Bagging, (b) boosting, (c) stacking.
Some software defect prediction studies have compared ensemble techniques to determine the best performing one [10, 18, 21, 39, 40]. In a study conducted by Wang et al. [18], different ensemble techniques such as bagging, boosting, random tree, random forest, random subspace, stacking, and voting were compared to each other and a single classifier (NB). According to the results, voting and random forest clearly exhibited better performance than others. In a different study [39], ensemble methods were compared with more than one base learner (NB, BN, SMO, PART, J48, RF, random tree, IB1, VFI, DT, NB tree). For boosted SMO, bagging J48, and boosting and bagging RT, performance of base classifiers was lower than that of ensemble learner classifiers.
In study [21], a new method was proposed of mixing feature selection and ensemble learning for defect classification. Results showed that random forests and the proposed algorithm are not affected by poor features, and the proposed algorithm outperforms existing single and ensemble classifiers in terms of classification performance. Another comparative study [10] used seven composite algorithms (Ave, Max, Bagging C4.5, bagging naive Bayes (NB), Boosting J48, Boosting naive Bayes, and RF) and one composite state-of-the art study for cross-project defect prediction. The Max algorithm yielded the best results regarding F-score in terms of classification performance.
Bowes et al. [40] compared RF, NB, Rpart, and SVM algorithms to determine whether these classifiers obtained the same results. The results demonstrated that a unique subset of defects can be discovered by specific classifiers. However, whereas some classifiers are steady in the predictions they make, other classifiers change in their predictions. As a result, ensembles with decision-making without majority voting can perform best.
One of the main problems of SDP is the imbalance between the defect and non-defect classes of the dataset. Generally, the number of defected instances is greater than the number of non-defected instances in the collected data. This situation causes the machine learning algorithms to perform poorly. Wang and Yao [19] compared five class-imbalanced learning methods (RUS, RUS-bal, THM, BNC, SMB) and NB and RF algorithms and proposed the dynamic version of AdaBoost.NC. They utilized balance, G-mean, and AUC measures for comparison. Results showed that AdaBoost.NC and naive Bayes are better than the other seven algorithms in terms of evaluation measures. Dynamic AdaBoost.NC showed better defect detection rate and overall performance than the original AdaBoost.NC. To handle the class imbalance problem, studies [20] have compared different methods (sampling, cost sensitive, hybrid, and ensemble) by taking into account evaluation metrics such as MCC and receiver operating characteristic (ROC).
As shown in Table 1, the most common datasets used in the defect prediction studies [17, 18, 19, 39] are the NASA MDP dataset and PROMISE repository datasets. In addition, some studies utilized open-source projects such as Bugzilla Columba and Eclipse JDT [26, 27], and other studies used Android application data [22, 23].
Although use of ensemble learning techniques has dramatically increased recently, studies that do not use ensemble learning are still conducted and successful. For example, in study [32], prediction models were created using source code metrics as in ensemble studies but by using different feature selection techniques such as genetic algorithm (GA).
To overcome the class imbalance problem, Tomar and Agarwal [33] proposed a prediction system that assigns lower cost to non-defective data samples and higher cost to defective samples to balance data distribution. In the absence of enough data within a project, required data can be obtained from cross projects; however, in this case, this situation may cause class imbalance. To solve this problem, Ryu and Baik [34] proposed multi-objective naïve Bayes learning for cross-project environments. To obtain significant software metrics on cloud computing environments, Ali et al. used a combination of filter and wrapper approaches [35]. They compared different machine learning algorithms such as NB, DT, and MLP [29, 37, 38, 41].
Software effort estimation (SEE) is critical for a company because hiring more employees than required will cause loss of revenue, while hiring fewer employees than necessary will result in delays in software project delivery. The estimation analysis helps to predict the amount of effort (in person hours) needed to develop a software product. Basic steps of software estimation can be itemized as follows:
Determine project objectives and requirements.
Design the activities.
Estimate product size and complexity.
Compare and repeat estimates.
SEE contains requirements and testing besides predicting effort estimation [42]. Many research and review studies have been conducted in the field of SEE. Recently, a survey [43] analyzed effort estimation studies that concentrated on ML techniques and compared them with studies focused on non-ML techniques. According to the survey, case-based reasoning (CBR) and artificial neural network (ANN) were the most widely used techniques. In 2014, Dave and Dutta [44] examined existing studies that focus only on neural network.
The current effort estimation studies using DM and ML techniques are available in Table 2. This table summarizes the prominent studies in terms of aspects such as year, data mining task, aim, datasets, and metrics. Table 2 indicates that neural network is the most widely used technique for the effort estimation task.
Ref. | Year | Task | Objective | Algorithms | Ensemble learning | Dataset | Evaluation metrics and results |
---|---|---|---|---|---|---|---|
[45] | 2008 | Regression | Ensemble of neural networks with associative memory (ENNA) | NN, MLP, KNN | Bagging | NASA, NASA 93, USC, SDR, Desharnais | MMRE, MdMRE and PRED(L) For ENNA PRED(25) = 36.4 For neural network PRED(25) = 8 |
[46] | 2009 | Regression | Authors proposed the ensemble of neural networks with associative memory (ENNA) | NN, MLP, KNN | Bagging | NASA, NASA 93, USC, SDR, Desharnais | Random subsampling, t-test MMRE, MdMRE, and PRED(L) ENNA is the best |
[47] | 2010 | Regression | To show the effectiveness of SVR for SEE | SVR, RBF | — | Tukutuku | LOOCV, MMRE, Pred(25), MEMRE, MdEMRE SVR outperforms others |
[48] | 2011 | Regression | To evaluate whether readily available ensemble methods enhance SEE | MLP, RBF, RT | Bagging | 5 datasets from PROMISE: cocomo81, nasa93, nasa, sdr, and Desharnais 8 datasets from ISBSG repository | MMRE, MdMRE, PRED(25) RTs and Bagging with MLPs perform similarly |
[49] | 2012 | Regression | To show the measures behave in SEE and to create good ensembles | MLP, RBF, REPTree, | Bagging | cocomo81, nasa93, nasa, cocomo2, desharnais, ISBSG repository | MMRE, PRED(25), LSD, MdMRE, MAE, MdAE Pareto ensemble for all measures, except LSD. |
[50] | 2012 | Regression | To use cross-company models to create diverse ensembles able to dynamically adapt to changes | WC RTs, CC-DWM | WC-DWM | 3 datasets from ISBSG repository (ISBSG2000, ISBSG2001, ISBSG) 2 datasets from PROMISE (CocNasaCoc81 and CocNasaCoc81Nasa93) | MAE, Friedman test Only DCL could improve upon RT CC data potentially beneficial for improving SEE |
[51] | 2012 | Regression | To generate estimates from ensembles of multiple prediction methods | CART, NN, LR, PCR, PLSR, SWR, ABE0-1NN, ABE0-5NN | Combining top M solo methods | PROMISE | MAR, MMRE, MdMRE, MMER, MBRE, MIBRE. Combinations perform better than 83% |
[52] | 2012 | Classification/regression | DM techniques to estimate software effort. | M5, CART, LR, MARS, MLPNN, RBFNN, SVM | — | Coc81, CSC, Desharnais, Cocnasa, Maxwell, USP05 | MdMRE, Pred(25), Friedman test Log + OLS > LMS, BC + OLS, MARS, LS-SVM |
[53] | 2013 | Clustering/classification | Estimation of software development effort | NN, ABE, C-means | — | Maxwell | 3-fold CV and LOOCV, RE, MRE, MMRE, PRED |
[54] | 2014 | Regression | ANNs are examined using COCOMO model | MLP, RBFNN, SVM, PSO-SVM Extreme learning Machines | — | COCOMO II Data | MMRE, PRED PSO-SVM is the best |
[55] | 2014 | — | A hybrid model based on GA And ACO for optimization | GA, ACO | — | NASA datasets | MMRE, the proposed method is the best |
[56] | 2015 | Regression | To display the effect of data preprocessing techniques on ML methods in SEE | CBR, ANN, CART Preprocessing rech: MDT, LD, MI, FS, CS, FSS, BSS | — | ISBSG, Desharnais, Kitchenham, USPFT | CV, MBRE, PRED (0.25), MdBRE |
[57] | 2016 | Regression | Four neural network models are compared with each other. | MLP, RBFNN, GRNN, CCNN | — | ISBSG repository | 10-fold CV, MAR The CCNN outperforms the other three models |
[58] | 2016 | Regression | To propose a model based on Bayesian network | GA and PSO | — | COCOMO NASA Dataset | DIR, DRM The proposed model is best |
[59] | 2016 | Classification/regression | A hybrid model using SVM and RBNN compared against previous models | SVM, RBNN | — | Dataset1 = 45 industrial projects Dataset2 = 65 educational projects | LOOCV, MAE, MBRE, MIBRE, SA The proposed approach is the best |
[60] | 2017 | Classification | To estimate software effort by using ML techniques | SVM, KNN | Boosting: kNN and SVM | Desharnais, Maxwell | LOOCV, k-fold CV ACC = 91.35% for Desharnais ACC = 85.48% for Maxwell |
Data mining and machine learning studies on the subject “effort estimation.”
Several studies have compared ensemble learning methods with single learning algorithms [45, 46, 48, 49, 51, 60] and examined them on cross-company (CC) and within-company (WC) datasets [50]. The authors observed that ensemble methods obtained by a proper combination of estimation methods achieved better results than single methods. Various ML techniques such as neural network, support vector machine (SVM), and k-nearest neighbor are commonly used as base classifiers for ensemble methods such as bagging and boosting in software effort estimation. Moreover, their results indicate that CC data can increase performance over WC data for estimation techniques [50].
In addition to the abovementioned studies, researchers have conducted studies without using ensemble techniques. The general approach is to investigate which DM technique has the best effect on performance in software effort estimation. For instance, Subitsha and Rajan [54] compared five different algorithms—MLP, RBFNN, SVM, ELM, and PSO-SVM—and Nassif et al. [57] investigated four neural network algorithms—MLP, RBFNN, GRNN, and CCNN. Although neural networks are widely used in this field, missing values and outliers frequently encountered in the training set adversely affect neural network results and cause inaccurate estimations. To overcome this problem, Khatibi et al. [53] split software projects into several groups based on their similarities. In their studies, the C-means clustering algorithm was used to determine the most similar projects and to decrease the impact of unrelated projects, and then analogy-based estimation (ABE) and NN were applied. Another clustering study by Azzeh and Nassif [59] combined SVM and bisecting k-medoids clustering algorithms; an estimation model was then built using RBFNN. The proposed method was trained on historical use case points (UCP).
Zare et al. [58] and Maleki et al. [55] utilized optimization methods for accurate cost estimation. In the former study, a model was proposed based on Bayesian network with genetic algorithm and particle swarm optimization (PSO). The latter study used GA to optimize the effective factors’ weight, and then trained by ant colony optimization (ACO). Besides conventional effort estimation studies, researchers have utilized machine learning techniques for web applications. Since web-based software projects are different from traditional projects, the effort estimation process for these studies is more complex.
It is observed that PRED(25) and MMRE are the most popular evaluation metrics in effort estimation. MMRE stands for the mean magnitude relative error, and PRED(25) measures prediction accuracy and provides a percentage of predictions within 25% of actual values.
Vulnerability analysis is becoming the focal point of system security to prevent weaknesses in the software system that can be exploited by an attacker. Description of software vulnerability is given in many different resources in different ways [61]. The most popular and widely utilized definition appears in the Common Vulnerabilities and Exposures (CVE) 2017 report as follows:
Vulnerability is a weakness in the computational logic found in software and some hardware components that, when exploited, results in a negative impact to confidentiality, integrity or availability.
Vulnerability analysis may require many different operations to identify defects and vulnerabilities in a software system. Vulnerabilities, which are a special kind of defect, are more critical than other defects because attackers exploit system vulnerabilities to perform unauthorized actions. A defect is a normal problem that can be encountered frequently in the system, easily found by users or developers and fixed promptly, whereas vulnerabilities are subtle mistakes in large codes [62, 63]. Wijayasekara et al. claim that some bugs have been identified as vulnerabilities after being publicly announced in bug databases [64]. These bugs are called “hidden impact vulnerabilities” or “hidden impact bugs.” Therefore, the authors proposed a hidden impact vulnerability identification methodology that utilizes text mining techniques to determine which bugs in bug databases are vulnerabilities. According to the proposed method, a bug report was taken as input, and it produces feature vector after applying text mining. Then, classifier was applied and revealed whether it is a bug or a vulnerability. The results given in [64] demonstrate that a large proportion of discovered vulnerabilities were first described as hidden impact bugs in public bug databases. While bug reports were taken as input in that study, in many other studies, source code is taken as input. Text mining is a highly preferred technique for obtaining features directly from source codes as in the studies [65, 66, 67, 68, 69]. Several studies [63, 70] have compared text mining-based models and software metrics-based models.
In the security area of software systems, several studies have been conducted related to DM and ML. Some of these studies are compared in Table 3, which shows the data mining task and explanation of the studies, the year they were performed, the algorithms that were used, the type of vulnerability analysis, evaluation metrics, and results. In this table, the best performing algorithms according to the evaluation criteria are shown in bold.
Ref. | Year | Task | Objective | Algorithms | Type | Dataset description | Evaluation metrics and results |
---|---|---|---|---|---|---|---|
[71] | 2011 | Clustering | Obtaining software vulnerabilities based on RDBC | RDBC | Static | Database is built by RD-Entropy | FNR, FPR |
[42] | 2011 | Classification/regression | To predict the time to next vulnerability | LR, LMS, MLP, RBF, SMO | Static | NVD, CPE, CVSS | CC, RMSE, RRSE |
[65] | 2012 | Text mining | Analysis of source code as text | RBF, SVM | Static | K9 email client for the Android platform | ACC, PR, recall ACC = 0.87, PR = 0.85, recall = 0.88 |
[64] | 2012 | Classification/text mining | To identify vulnerabilities in bug databases | — | Static | Linux kernel MITRE CVE and MySQL bug databases | BDR, TPR, FPR 32% (Linux) and 62% (MySQL) of vulnerabilities |
[72] | 2014 | Classification/regression | Combine taint analysis and data mining to obtain vulnerabilities | ID3, C4.5/J48, RF, RT, KNN, NB, Bayes Net, MLP, SVM, LR | Hybrid | A version of WAP to collect the data | 10-fold CV, TPD, ACC, PR, KAPPA ACC = 90.8%, PR = 92%, KAPPA = 81% |
[73] | 2014 | Clustering | Identify vulnerabilities from source codes using CPG | — | Static | Neo4J and InfiniteGraph databases | — |
[63] | 2014 | Classification | Comparison of software metrics with text mining | RF | Static | Vulnerabilities from open-source web apps (Drupal, Moodle, PHPMyAdmin) | 3-fold CV, recall, IR, PR, FPR, ACC. Text mining provides benefits overall |
[69] | 2014 | Classification | To create model in the form of a binary classifier using text mining | NB, RF | Static | Applications from the F-Droid repository and Android | 10-fold CV, PR, recall PR and recall ≥ 80% |
[74] | 2015 | Classification | A new approach (VCCFinder) to obtain potentially dangerous codes | SVM-based detection model | — | The database contains 66 GitHub projects | k-fold CV, false alarms <99% at the same level of recall |
[70] | 2015 | Ranking/classification | Comparison of text mining and software metrics models | RF | — | Vulnerabilities from open-source web apps (Drupal, Moodle, PHPMyAdmin) | 10-fold CV Metrics: ER-BCE, ERBPP, ER-AVG |
[75] | 2015 | Clustering | Search patterns for taint-style vulnerabilities in C code | Hierarchical clustering (complete-linkage) | Static | 5 open-source projects: Linux, OpenSSL, Pidgin, VLC, Poppler (Xpdf) | Correct source, correct sanitization, number of traversals, generation time, execution time, reduction, amount of code review <95% |
[76] | 2016 | Classification | Static and dynamic features for classification | LR, MLP, RF | Hybrid | Dataset was created by analyzing 1039 test cases from the Debian Bug Tracker | FPR, FNR Detect 55% of vulnerable programs |
[77] | 2017 | Classification | 1. Employ a deep neural network 2. Combine N-gram analysis and feature selection | Deep neural network | — | Feature extraction from 4 applications (BoardGameGeek, Connectbot, CoolReader, AnkiDroid) | 10 times using 5-fold CV ACC = 92.87%, PR = 94.71%, recall = 90.17% |
[67] | 2017 | Text mining | To analyze characteristics of software vulnerability from source files | — | — | CVE, CWE, NVD databases | PR = 70%, recall = 60% |
[68] | 2017 | Text mining | Deep learning (LSTM) is used to learn semantic and syntactic features in code | RNN, LSTM, DBN | — | Experiments on 18 Java applications from the Android OS platform | 10-fold CV, PR, recall, and F-score Deep Belief Network PR, recall, and F-score > 80% |
[66] | 2018 | Classification | Identify bugs by extracting text features from C source code | NB, KNN, K-means, NN, SVM, DT, RF | Static | NVD, Cat, Cp, Du, Echo, Head, Kill, Mkdir, Nl, Paste, Rm, Seq, Shuf, Sleep, Sort, Tail, Touch, Tr, Uniq, Wc, Whoami | 5-fold CV ACC, TP, TN ACC = 74% |
[78] | 2018 | Regression | A deep learning-based vulnerability detection system (VulDeePecker) | BLSTM NN | Static | NIST: NVD and SAR project | 10-fold CV, PR, recall, F-score F-score = 80.8% |
[79] | 2018 | Classification | A mapping between existing requirements and vulnerabilities | LR, SVM, NB | — | Data is gathered from Apache Tomcat, CVE, requirements from Bugzilla, and source code is collected from Github | PR, recall, F-score LSI > SVM |
Data mining and machine learning studies on the subject “vulnerability analysis.”
Vulnerability analysis can be categorized into three types: static vulnerability analysis, dynamic vulnerability analysis, and hybrid analysis [61, 80]. Many studies have applied the static analysis approach, which detects vulnerabilities from source code without executing software, since it is cost-effective. Few studies have performed the dynamic analysis approach, in which one must execute software and check program behavior. The hybrid analysis approach [72, 76] combines these two approaches.
As revealed in Table 3, in addition to classification and text mining, clustering techniques are also frequently seen in software vulnerability analysis studies. To detect vulnerabilities in an unknown software data repository, entropy-based density clustering [71] and complete-linkage clustering [75] were proposed. Yamaguchi et al. [73] introduced a model to represent a large number of source codes as a graph called control flow graph (CPG), a combination of abstract syntax tree, CFG, and program dependency graph (PDG). This model enabled the discovery of previously unknown (zero-day) vulnerabilities.
To learn the time to next vulnerability, a prediction model was proposed in the study [42]. The result could be a number that refers to days or a bin representing values in a range. The authors used regression and classification techniques for the former and latter cases, respectively.
In vulnerability studies, issue tracking systems like Bugzilla, code repositories like Github, and vulnerability databases such as NVD, CVE, and CWE have been utilized [79]. In addition to these datasets, some studies have used Android [65, 68, 69] or web [63, 70, 72] (PHP source code) datasets. In recent years, researchers have concentrated on deep learning for building binary classifiers [77], obtaining vulnerability patterns [78], and learning long-term dependencies in sequential data [68] and features directly from the source code [81].
Li et al. [78] note two difficulties of vulnerability studies: demanding, intense manual labor and high false-negative rates. Thus, the widely used evaluation metrics in vulnerability analysis are false-positive rate and false-negative rate.
During the past years, software developers have used design patterns to create complex software systems. Thus, researchers have investigated the field of design patterns in many ways [82, 83]. Fowler defines a pattern as follows:
“
Patterns display relationships and interactions between classes or objects. Well-designed object-oriented systems have various design patterns integrated into them. Design patterns can be highly useful for developers when they are used in the right manner and place. Thus, developers avoid recreating methods previously refined by others. The pattern approach was initially presented in 1994 by four authors—namely, Erich Gama, Richard Helm, Ralph Johnson, and John Vlissides—called the Gang of Four (GOF) in 1994 [85]. According to the authors, there are three types of design patterns:
Creational patterns provide an object creation mechanism to create the necessary objects based on predetermined conditions. They allow the system to call appropriate object and add flexibility to the system when objects are created. Some creational design patterns are factory method, abstract factory, builder, and singleton.
Structural patterns focus on the composition of classes and objects to allow the establishment of larger software groups. Some of the structural design patterns are adapter, bridge, composite, and decorator.
Behavioral patterns determine common communication patterns between objects and how multiple classes behave when performing a task. Some behavioral design patterns are command, interpreter, iterator, observer, and visitor.
Many design pattern studies exist in the literature. Table 4 shows some design pattern mining studies related to machine learning and data mining. This table contains the aim of the study, mining task, year, and design patterns selected by the study, input data, dataset, and results of the studies.
Ref. | Year | Task | Objective | Algorithms | EL | Selected design patterns | Input data | Dataset | Evaluation metrics and results |
---|---|---|---|---|---|---|---|---|---|
[86] | 2012 | Text classification | Two-phase method: 1—text classification to 2—learning design patterns | NB, KNN, DT, SVM | — | 46 security patterns, 34 Douglass patterns, 23 GoF patterns | Documents | Security, Douglass, GoF | PR, recall, EWM PR = 0.62, recall = 0.75 |
[87] | 2013 | Regression | An approach is to find a valid instance of a DP or not | ANN | — | Adapter, command, composite, decorator, observer, and proxy | Set of candidate classes | JHotDraw 5.1 open-source application | 10 fold CV, PR, recall |
[88] | 2014 | Graph mining | Sub-graph mining-based approach | CloseGraph | — | — | Java source code | Open-source project:YARI, Zest, JUnit, JFreeChart, ArgoUML | No any empirical comparison |
[89] | 2015 | Classification/clustering | MARPLE-DPD is developed to classify instances whether it is a bad or good instance | SVM, DT, RF, K-means, ZeroR, OneR, NB, JRip, CLOPE. | — | Classification for singleton and adapter Classification and clustering for composite, decorator, and factory method | — | 10 open-source software systems DPExample, QuickUML 2001, Lexi v0.1.1 alpha, JRefactory v2.6.24, Netbeans v1.0.x, JUnit v3.7, JHotDraw v5.1, MapperXML v1.9.7, Nutch v0.4, PMD v1.8 | 10-fold CV, ACC, F-score, AUC ACC > =85% |
[90] | 2015 | Regression | A new method (SVM-PHGS) is proposed | Simple Logistic, C4.5, KNN, SVM, SVM-PHGS | — | Adapter, builder, composite, factory method, iterator, observer | Source code | P-mart repository | PR, recall, F-score, FP PR = 0.81, recall =0.81, F-score = 0.81, FP = 0.038 |
[91] | 2016 | Classification | Design pattern recognition using ML algorithms. | LRNN, DT | — | Abstract factory, adapter patterns | Source code | Dataset with 67 OO metrics, extracted by JBuilder tool | 5-fold CV, ACC, PR, recall, F-score ACC = 100% by LRNN |
[92] | 2016 | Classification | Three aspects: design patterns, software metrics, and supervised learning methods | Layer Recurrent Neural Network (LRNN) | RF | Abstract factory, adapter, bridge, singleton, and template method | Source code | Dataset with 67 OO metrics, extracted by JBuilder tool | PR, recall, F-score F-score = 100% by LRNN and RF ACC = 100% by RF |
[93] | 2017 | Classification | 1. Creation of metrics-oriented dataset 2. Detection of software design patterns | ANN, SVM | RF | Abstract factory, adapter, bridge, composite, and Template | Source code | Metrics extracted from source codes (JHotDraw, QuickUML, and Junit) | 5-fold and 10-fold CV, PR, recall, F-score ANN, SVM, and RF yielded to 100% PR for JHotDraw |
[94] | 2017 | Classification | Detection of design motifs based on a set of directed semantic graphs | Strong graph simulation, graph matching | — | All three groups: creational, structural, behavioral | UML class diagrams | — | PR, recall High accuracy by the proposed method |
[95] | 2017 | Text categorization | Selection of more appropriate design patterns | Fuzzy c-means | Ensemble-IG | Various design patterns | Problem definitions of design patterns | DP, GoF, Douglass, Security | F-score |
[96] | 2018 | Classification | Finding design pattern and smell pairs which coexist in the code | J48 | — | Used patterns: adapter, bridge, Template, singleton | Source code | Eclipse plugin Web of Patterns The tool selected for code smell detection is iPlasma | PR, recall, F-score, PRC, ROC Singleton pattern shows no presence of bad smells |
Data mining and machine learning studies on the subject “design pattern mining.”
In design pattern mining, detecting the design pattern is a frequent study objective. To do so, studies have used machine learning algorithms [87, 89, 90, 91], ensemble learning [95], deep learning [97], graph theory [94], and text mining [86, 95].
In study [91], the training dataset consists of 67 object-oriented (OO) metrics extracted by using the JBuilder tool. The authors used LRNN and decision tree techniques for pattern detection. Alhusain et al. [87] generated training datasets from existing pattern detection tools. The ANN algorithm was selected for pattern instances. Chihada et al. [90] created training data from pattern instances using 45 OO metrics. The authors utilized SVM for classifying patterns accurately. Another metrics-oriented dataset was developed by Dwivedi et al. [93]. To evaluate the results, the authors benefited from three open-source software systems (JHotDraw, QuickUML, and JUnit) and applied three classifiers, SVM, ANN, and RF. The advantage of using random forest is that it does not require linear features and can manage high-dimensional spaces.
To evaluate methods and to find patterns, open-source software projects such as JHotDraw, Junit, and MapperXML have been generally preferred by researchers. For example, Zanoni et al. [89] developed a tool called MARPLE-DPD by combining graph matching and machine learning techniques. Then, to obtain five design patterns, instances were collected from 10 open-source software projects, as shown in Table 4.
Design patterns and code smells are related issues: Code smell refers to symptoms in code, and if there are code smells in a software, its design pattern is not well constructed. Therefore, Kaur and Singh [96] checked whether design pattern and smell pairs appear together in a code by using J48 Decision Tree. Their obtained results showed that the singleton pattern had no presence of bad smells.
According to the studies summarized in the table, the most frequently used patterns are abstract factory and adapter. It has recently been observed that studies on ensemble learning in this field are increasing.
One of the SE tasks most often used to improve the quality of a software system is refactoring, which Martin Fowler has described as “a technique for restructuring an existing body of code, altering its internal structure without changing its external behavior” [98]. It improves readability and maintainability of the source code and decreases complexity of a software system. Some of the refactoring types are: Add Parameter, Replace Parameter, Extract method, and Inline method [99].
Code smell and refactoring are closely related to each other: Code smells represent problems due to bad design and can be fixed during refactoring. The main challenge is to obtain which part of the code needs refactoring.
Some of data mining studies related to software refactoring are presented in Table 5. Some studies focus on historical data to predict refactoring [100] or to obtain both refactoring and software defects [101] using different data mining algorithms such as LMT, Rip, and J48. Results suggest that when refactoring increases, the number of software defects decreases, and thus refactoring has a positive effect on software quality.
Ref. | Year | Task | Objective | Algorithms | EL | Dataset | Evaluation metrics and results |
---|---|---|---|---|---|---|---|
[100] | 2007 | Regression | Stages: (1) data understanding, (2) preprocessing, (3) ML, (4) post-processing, (5) analysis of the results | J48, LMT, Rip, NNge | — | ArgoUML, Spring Framework | 10-fold CV, PR, recall, F-score PR and recall are 0.8 for ArgoUML |
[101] | 2008 | Classification | Finding the relationship between refactoring and defects | C4.5, LMT, Rip, NNge | — | ArgoUML, JBoss Cache, Liferay Portal, Spring Framework, XDoclet | PR, recall, F-score |
[102] | 2014 | Regression | Propose GA-based learning for software refactoring based on ANN | GA, ANN | — | Xerces-J, JFreeChart, GanttProject, AntApache, JHotDraw, and Rhino. | Wilcoxon test with a 99% confidence level (α = 0.01) |
[103] | 2015 | Regression | Removing defects with time series in a multi-objective approach | Multi-objective algorithm, based on NSGA-II, ARIMA | FindBugs, JFreeChart, Hibernate, Pixelitor, and JDI-Ford | Wilcoxon rank sum test with a 99% confidence level (α < 1%) | |
[104] | 2016 | Web mining/clustering | Unsupervised learning approach to detect refactoring opportunities in service-oriented applications | PAM, K-means, COBWEB, X-Means | — | Two datasets of WSDL documents | COBWEB and K-means max. 83.33% and 0%, inter-cluster COBWEB and K-means min. 33.33% and 66.66% intra-cluster |
[105] | 2017 | Clustering | A novel algorithm (HASP) for software refactoring at the package level | Hierarchical clustering algorithm | — | Three open-source case studies | Modularization Quality and Evaluation Metric Function |
[99] | 2017 | Classification | A technique to predict refactoring at class level | PCA, SMOTE LS-SVM, RBF | — | From tera- PROMISE Repository seven open-source software systems | 10-fold CV, AUC, and ROC curves RBF kernel outperforms linear and polynomial kernel The mean value of AUC for LS-SVM RBF kernel is 0.96 |
[106] | 2017 | Classification | Exploring the impact of clone refactoring (CR) on the test code size | LR, KNN, NB | RF | data collected from an open-source Java software system (ANT) | PR, recall, accuracy, F-score kNN and RF outperform NB ACC (fitting (98%), LOOCV (95%), and 10 FCV (95%)) |
[107] | 2017 | — | Finding refactoring opportunities in source code | J48, BayesNet, SVM, LR | RF | Ant, ArgoUML, jEdit, jFreeChart, Mylyn | 10-fold CV, PR, recall 86–97% PR and 71–98% recall for proposed tech |
[108] | 2018 | Classification | A learning-based approach (CREC) to extract refactored and non-refactored clone groups from repositories | C4.5, SMO, NB. | RF, Adaboost | Axis2, Eclipse.jdt.core, Elastic Search, JFreeChart, JRuby, and Lucene | PR, recall, F-score F-score = 83% in the within-project F-score = 76% in the cross-project |
[109] | 2018 | Clustering | Combination of the use of multi-objective and unsupervised learning to decrease developer’s effort | GMM, EM | — | ArgoUML, JHotDraw, GanttProject, UTest, Apache Ant, Azureus | One-way ANOVA with a 95% confidence level (α = 5%) |
Data mining and machine learning studies on the subject “refactoring.”
While automated refactoring does not always give the desired result, manual refactoring is time-consuming. Therefore, one study [109] proposed a clustering-based recommendation tool by combining multi-objective search and unsupervised learning algorithm to reduce the number of refactoring options. At the same time, the number of refactoring that should be selected is decreasing with the help of the developer’s feedback.
Since many SE studies that apply data mining approaches exist in the literature, this article presents only a few of them. However, Figure 4 shows the current number of papers obtained from the Scopus search engine for each year from 2010 to 2019 by using queries in the title/abstract/keywords field. We extracted publications in 2020 since this year has not completed yet. Queries included (“data mining” OR “machine learning”) with (“defect prediction” OR “defect detection” OR “bug prediction” OR “bug detection”) for defect prediction, (“effort estimation” OR “effort prediction” OR “cost estimation”) for effort estimation, (“vulnerab*” AND “software” OR “vulnerability analysis”) for vulnerability analysis, and (“software” AND “refactoring”) for refactoring. As seen in the figure, the number of studies using data mining in SE tasks, especially defect prediction and vulnerability analysis, has increased rapidly. The most stable area in the studies is design pattern mining.
Number of publications of data mining studies for SE tasks from Scopus search by their years.
Figure 5 shows the publications studied in classification, clustering, text mining, and association rule mining as a percentage of the total number of papers obtained by a Scopus query for each SE task. For example, in defect prediction, the number of studies is 339 in the field of classification, 64 in clustering, 8 in text mining, and 25 in the field of association rule mining. As can be seen from the pie charts, while clustering is a popular DM technique in refactoring, no study related to text mining is found in this field. In other SE tasks, the preferred technique is classification, and the second is clustering.
Number of publications of data mining studies for SE tasks from Scopus search by their topics.
Defect prediction generally compares learning algorithms in terms of whether they find defects correctly using classification algorithms. Besides this approach, in some studies, clustering algorithms were used to select futures [110] or to compare supervised and unsupervised methods [27]. In the text mining area, to extract features from scripts, TF-IDF techniques were generally used [111, 112]. Although many different algorithms have been used in defect prediction, the most popular ones are NB, MLP, and RBF.
Figure 6 shows the number of document types (conference paper, book chapter, article, book) published between the years of 2010 and 2019. It is clearly seen that conference papers and articles are the most preferred research study type. It is clearly seen that there is no review article about data mining studies in design pattern mining.
The number of publications in terms of document type between 2010 and 2019.
Table 6 shows popular repositories that contain various datasets and their descriptions, which tasks they are used for, and hyperlinks to download. For example, the PMART repository includes source files of java projects, and the PROMISE repository has different datasets with software metrics such as cyclomatic complexity, design complexity, and lines of code. Since these repositories contain many datasets, no detailed information about them has been provided in this article.
Repository | Topic | Description | Web link |
---|---|---|---|
Nasa MDP | Defect Pred. | NASA’s Metrics Data Program | https://github.com/opensciences/opensciences.github.io/tree/master/repo/defect/mccabehalsted/_posts |
Android Git | Defect Pred. | Android version bug reports | https://android.googlesource.com/ |
PROMISE | Defect Pred. Effort Est. | It includes 20 datasets for defect prediction and cost estimation | http://promise.site.uottawa.ca/SERepository/datasets-page.html |
Software Defect Pred. Data | Defect Pred. | It includes software metrics, # of defects, etc. Eclipse JDT: Eclipse PDE: | http://www.seiplab.riteh.uniri.hr/?page_id=834&lang=en |
PMART | Design pattern mining | It has 22 patterns 9 Projects, 139 ins. Format: XML Manually detected and validated | http://www.ptidej.net/tools/designpatterns/ |
Description of popular repositories used in studies.
Refactoring can be applied at different levels; study [105] predicted refactoring at package level using hierarchical clustering, and another study [99] applied class-level refactoring using LS-SVM as learning algorithm, SMOTE for handling refactoring, and PCA for feature extraction.
Data mining techniques have been applied successfully in many different domains. In software engineering, to improve the quality of a product, it is highly critical to find existing deficits such as bugs, defects, code smells, and vulnerabilities in the early phases of SDLC. Therefore, many data mining studies in the past decade have aimed to deal with such problems. The present paper aims to provide information about previous studies in the field of software engineering. This survey shows how classification, clustering, text mining, and association rule mining can be applied in five SE tasks: defect prediction, effort estimation, vulnerability analysis, design pattern mining, and refactoring. It clearly shows that classification is the most used DM technique. Therefore, new studies can focus on clustering on SE tasks.
LMT | logistic model trees |
Rip | repeated incremental pruning |
NNge | nearest neighbor generalization |
PCA | principal component analysis |
PAM | partitioning around medoids |
LS-SVM | least-squares support vector machines |
MAE | mean absolute error |
RBF | radial basis function |
RUS | random undersampling |
SMO | sequential minimal optimization |
GMM | Gaussian mixture model |
EM | expectation maximizaion |
LR | logistic regression |
SMB | SMOTEBoost |
RUS-bal | balanced version of random undersampling |
THM | threshold-moving |
BNC | AdaBoost.NC |
RF | random forest |
RBF | radial basis function |
CC | correlation coefficient |
ROC | receiver operating characteristic |
BayesNet | Bayesian network |
SMOTE | synthetic minority over-sampling technique |
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\r\n\tPollution is caused by a wide variety of human activities and occurs in diverse forms, for example biological, chemical, et cetera. In recent years, significant efforts have been made to ensure that the environment is clean, that rigorous rules are implemented, and old laws are updated to reduce the risks towards humans and ecosystems. However, rapid industrialization and the need for more cultivable sources or habitable lands, for an increasing population, as well as fewer alternatives for waste disposal, make the pollution control tasks more challenging. Therefore, this topic will focus on assessing and managing environmental pollution. It will cover various subjects, including risk assessment due to the pollution of ecosystems, transport and fate of pollutants, restoration or remediation of polluted matrices, and efforts towards sustainable solutions to minimize environmental pollution.
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",annualVolume:11967,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/39.jpg",editor:{id:"137040",title:"Prof.",name:"Jose",middleName:null,surname:"Navarro-Pedreño",fullName:"Jose Navarro-Pedreño",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRAXrQAO/Profile_Picture_2022-03-09T15:50:19.jpg",institutionString:"Miguel Hernández University of Elche, Spain",institution:null},editorTwo:null,editorThree:null,editorialBoard:[{id:"177015",title:"Prof.",name:"Elke Jurandy",middleName:null,surname:"Bran Nogueira Cardoso",fullName:"Elke Jurandy Bran Nogueira Cardoso",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRGxzQAG/Profile_Picture_2022-03-25T08:32:33.jpg",institutionString:"Universidade de São Paulo, Brazil",institution:null},{id:"211260",title:"Dr.",name:"Sandra",middleName:null,surname:"Ricart",fullName:"Sandra Ricart",profilePictureURL:"https://mts.intechopen.com/storage/users/211260/images/system/211260.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}}]},{id:"40",title:"Ecosystems and Biodiversity",keywords:"Ecosystems, Biodiversity, Fauna, Taxonomy, Invasive species, Destruction of habitats, Overexploitation of natural resources, Pollution, Global warming, Conservation of natural spaces, Bioremediation",scope:"