Data mining and machine learning studies on the subject “defect prediction.”
\r\n\tTo sum up, there are numerous engineering applications of diamond which are yet to be realized and this book will address some of the mentioned and hopefully open some new topics.
\r\n\t
Although ultrasounds (US) were discovered in the 18th Century due to their use in animal kingdom, they were not manufactured until the 19th Century, when certain devices facilitating the reproduction of these non audible for human sounds were developed. They constitute rare frequencies with several properties. First of all they were developed for their use in navy and in medicine. In the 20th Century it was noticed that they could have uses in dentistry, so the first applications for calculus removal were initiated, taking advantage of their mechanical energy and cavitation effect. The different possibilities achieved by conventional US together with those of sonicators, of lower frequency but with similar effects, resulted in a fast development of these technologies.
Since Michigan longitudinal studies demonstrated that the open flap radicular instrumentation techniques were in a long term as effective as the closed ones, the latter were developed, so treatment of periodontitis suffered a change of paradigm. From that moment on, periodontal treatment involved less open flaps and more mechanical treatments, limiting surgeries to very concrete cases, in order to enable access to the deepest pockets and furcations. The result was a reduction in discomfort for patients and a better long term prognosis. Prevention gained more importance and supportive periodontal therapies were regularly done adjusting them to the individual necessities of each patient, depending on the type of periodontitis and the severity of the case. To reduce the number of surgeries, it was crucial to develop instruments able to reach deep pockets. Small curettes and microcurettes were developed, and later on special ultrasonic tips which allowed the instrumentation of pockets of difficult access for Gracey and Universal curettes. Even when effectuating periodontal surgery, clinicians preferred US rather than curettes for the narrow furcations´ instrumentation. The fewer fatigue of the professional and the efficacy of the results have favoured the great development of these instruments during the last years.
A new progress occurred in dentistry with the introduction of piezoelectric US. These US produced less discomfort in patients, and with the development of special tips imitating microcurettes, deep and narrow pockets instrumentation was possible without doing surgery. With the important development of implant rehabilitations during the last twenty years and the subsequent peri-implantitis, the necessity of new instruments has arisen, as traditional and teflon curettes are not suitable for this purpose. To solve this problem, tips of teflon and other materials have emerged to facilitate the elimination of deposits settled over the irregular implants´ surface, with controversial results.
The use of US in endodontics was introduced later to clean and disinfect root canals. It is quite useful basically to make easier the access to the root canals in certain conditions, in endodontic retreatments and to clean before the sealing of the root canal. One of the latest US applications in dentistry is in surgery, as they avoid discomfort of rotary instruments while preserving the soft tissues. The cut precision allows their use in implants´ surgery, ostectomies and especially in those techniques where tearing of soft tissues could be produced due to their proximity, i.e., sinus lift procedures. These techniques are in continuous progress; they are linked to piezoelectric US and to those new materials allowing their use in favourable conditions.
The aim of this chapter is to revise the physical principles of US, the materials used and the historical evolution, their basic uses in perio and endodontics, as well as their efficacy when comparing with other techniques and finally the possibilities in maxilar surgery. Other less frequent applications are also mentioned.
Before 1700 man was unaware of ultrasounds because their frequency is below human´s audible frequency. In 1700, Spallanzani described their use by bats when flying and capturing their preys. Later on, it was demonstrated that other animal species had the same faculties, and in the 19th Century, with the discovery of Doppler effect about deformation of light waves in movement, it was observed that this property could also be applied to ultrasounds. In fact, they are sound waves that are not audible for men due to their high frequency (Figure 1).
Human audition and ultrasound frequencies in Hz
At the end of the 19th Century, the Curie brothers [1] described the piezoelectric effect (from greek piezein, mechanic pressure) of several crystals, property used later for the fabrication of ultrasonic devices with new characteristics. At this time, in 1883, Galton develops a high frequency whistle to find out the human hearing limit, and from that moment on ultrasounds (US) for different applications are developed. Although the first ultrasonic apparatus date from 1950, the first commercial application for dentistry was in periodontics in 1957 with Cavitron®, developed by Dentsply for doing prophylaxis and calculus removal. Its name comes from the cavitation effect produced by ultrasounds when working with water. When a liquid flows through a region where pressure is lower than its steam pressure, the liquid boils and produces vapour bubbles. The bubbles will be carried to a higher pressure area, where the steam returns immediately to the liquid phase, imploding the bubbles suddenly. Thus, a change from liquid to gaseous phase takes place, and again to liquid phase with water dissociation and formation of H+and OH-(Figure 2).
Representation of cavitation effect
Cavitation is defined as the formation of submicroscopic cavities or vacuums as a result of the vibration of a fluid due to the high frequency alternating movement of the tip of an instrument. When these vacuums implode, shock waves which spread through the medium are generated and produce energy (heat) release [2].
The basis of the ultrasonic action consists of an electric generator transmitting vibrations to the tip of the device with frequencies of 25,000 to 30,000 Hz, whose shock waves generate pressures and depressions which detach the calculus and break water molecules by the cavitation phenomenon. To the effect of cavitation it adds an acoustic streaming, with a great cleaning and bactericidal action, which potentiates the bactericidal effect of cavitation, effect that can increase adding an antiseptic product to the irrigation fluid.
There are two types of ultrasonic devices: the classical ones, laminated or magnetostrictive, with elliptical oscillation of the tip, and the piezoelectric ones, of quartz with lineal oscillation. Laminated US are based on the Joule magnetostriction phenomenon. According to this phenomenon, several ferromagnetic materials get deformed when they go through a magnetic field. The deformation degree depends on the material employed, the magnetization strength, the previous treatment of the material and the temperature. The metallic sheets are situated in the handle, i.e. in the handpiece where the insert is placed (Figure 3).
Laminated US device and several ultrasonic inserts for Cavitron
Piezoelectric US (Figure 4) are based on quartz clock principles. When applying an alternating current to the ceramic/quartz discs, changes in polarity produce expansion and contraction trasmitting the oscillation to the tip, applicator or insert. The sound thus generated, presents the same intensity, frequency and wavelength than the material employed in its fabrication (quartz, zinc blende, sodium borate...). Nowadays, the most used crystals are ceramic zirconate discs, which are less sensitive to temperature and blows.
Piezoelectric US for surgery. Modified from Variosurg (NSK) catalogue
Oscillation of magnetostrictive US, piezoceramic US and sonicators
US present several effects over the tissues which vary depending on the time, type of US and way of application. These effects are mechanical, thermal, biological, chemical, massage and placebo.
Mechanical effects. The most important, as vibration favours the removal of calculus, biofilm and of the cementum surface, damaged by bacterial toxins and sometimes contaminated by bacteria (Figure 6). Inside the root canals, US clean the pulpal detritus.
Bacterial presence inside cementum in periodontitis. Original magnification SEM x3000. Bacteria can be identified supragingivally, in the epithelial junction and in apical areas of cementum
Thermal effects. US are a way of energy and thus, during their application, heat is generated. This heat can be useful, as it favours the cleaning of the treated area and the elimination of detritus, blood debris, biofilm and calculus; but if it is excessive it could burn the tissues, especially gingiva and periostium. This is the reason why it is crucial to control the irrigation system, checking for possible obstructions of applicator/insert.
Biological effects. US produce an increase in permeability of the cellular membrane, known as phonophoresis, which facilitates the cellular function, and thus the recuperation of the inflamed soft tissues.
Chemical effects. Ultrasonic vibration favours the chemical processes in the area in which they are applied. Biological exchanges among the treated tissues improve; in addition, an increase of the blood supply takes place, helping to reduce inflammation and to facilitate the arrival of blood cells and anti-inflammatory mediators, favouring tissue normality. It also produces oxidation and macromolecule depolymerization phenomena, due to the ions release.
The massage and placebo effects, also associated to US, are of less interest in our field, but they should not be forgotten.
Due to the cavitation effect and the acoustic micro-streaming produced by oscillatory movements of ultrasonic inserts, US are used in humans in different ways for diagnosis and treatment. In the oral cavity they are mainly used for root instrumentation in periodontics, and less in endodontics, ostectomy, and sinus lift procedures. There are also other less frequent applications that we shall describe.
It is well known that periodontal disease is based on the presence of a mature biofilm with more than 700 bacterial species, being only a fraction of them related to periodontitis. The progression of the disease depends on the periodontopathogens, but also on the patient´s immune system and its response to bacterial aggression. The elimination of bacteria, their toxins and calculus produced by saliva, is essential to keep under control the disease. Once local factors are removed, a strict hygiene is required, as well as a supportive periodontal treatment program, in order to eliminate calculus and subgingival biofilm, which is the main responsible of the bone and attachment loss and is formed shortly after its elimination.
Treatment was traditionally based on the mechanical elimination of plaque and calculus, which facilitate biofilm´s survival, mainly using hand instruments and US, directly or by an open flap procedure. Longitudinal studies of the decades of 70´s and 80´s, showed that even most periodontally advanced cases, well treated and maintained, remained stable through the years [3], versus those patients who did not receive any treatment, who suffered a considerable tooth loss and worsening of periodontal parameters [4].
Since Michigan longitudinal studies [5-7] demonstrated that the open flap radicular instrumentation techniques were in a long term as effective as the closed ones [7], the latter were developed, so treatment of periodontitis suffered a change of paradigm. From that moment on, periodontal treatment involved less open flaps and more mechanical treatments, limiting surgeries to very concrete cases, in order to enable access to the most deep pockets and furcations [8]. The result was a reduction in discomfort for patients and a better long term prognosis. Prevention gained more importance and supportive periodontal therapies were regularly done adjusting them to the individual necessities of each patient, depending on the type of periodontitis and the severity of the case.
To reduce the number of surgeries, it was crucial to develop instruments able to reach deep pockets. Small curettes and microcurettes were developed, and later on special ultrasonic tips which allowed the instrumentation of pockets of difficult access for Gracey and Universal curettes.
The first device used in periodontal prophylaxis was Cavitron®, introduced in 1957 by Dentsply (USA). With the important development of implant rehabilitations during the last twenty years and the subsequent peri-implantitis, the necessity of new instruments has arisen, as traditional and teflon curettes are not suitable for this purpose. Ultrasonic instruments are very comfortable to use, they produce less fatigue in the operator than curettes and allow the combination of different tips and products in order to improve the treatment efficacy. Several authors [9] even demonstrate better results when instrumentation is done with US instead of curettes.
During the 80´s, we demonstrated in several publications that prophylaxis done in vitro with US resulted at least equal or even more effective than with curettes [10, 11] (Figure 7).
Cementum of the same tooth treated with curettes (left) and US (right). Original magnification SEM x352, x1136 and x3000
In Drisko´s 1993 review, it is suggested that a thorough radicular debridement can be achieved without overinstrumentation, using certain sonic and ultrasonic scalers. The evaluation of residual plaque and calculus after hand and mechanical instrumentation with sonic and ultrasonic scalers, shows that sonic and US instruments obtain similar, and in some cases, better results than those obtained with manual instrumentation. When comparing modified ultrasonic inserts with unmodified ultrasonic inserts and manual scalers, it is observed that the modified ones generate smoother surfaces, better plaque and calculus removal, less damage and better access to the bottom of the pocket, which together with a less operating time lead to a lower fatigue [12].
Several years later, another review of the same author shows that US, through their cavitation effect, are able to eliminate toxins from the cementum surface without damaging it. This, together with the irrigation action, improves healing, as it is not necessary an excessive instrumentation of cementum to achieve satisfactory results. The additional benefits of the chemical irrigation during ultrasonic instrumentation are the weakly attached subgingival plaque removal and a better access to difficult areas such as narrow and deep pockets, root grooves and furcations. Thus, microultrasonic tips, of smaller diameter, allow the penetration 1 mm farther than manual instruments [13].
In a position paper of 2000, US and sonicators were compared, reaching similar results than hand instruments in terms of plaque, calculus and endotoxins removal. Ultrasonic scalers used at medium power produced less damage in root surfaces than manual instruments or sonicators. Furcations seemed to be more accesible when using sonic or ultrasonic scalers than when using manual instruments. It was still not clear if root roughness was more or less pronounced when using US or curettes, and if the roughness produced in radicular cement affected long term wound healing. Although the aim of root instrumentation is the highest as possible elimination of calculus and toxins, it is necessary to preserve cementum. According to the reviewed papers, toxins remain in the root surface, thus being easily removed with US. One of the main problems of the intervention with US and sonicators is the aerosols production, which involves the risk of transmitting infectious diseases, therefore it is essential the use of barriers against aerosols. Concerning the use of chemical agents there is no evidence of their additional clinical benefit [14].
To avoid the potential damage of the cementum surface done by sonic and US instruments and curettes, and looking after an effective treatment of the root surface, a sonic instrument covered by teflon was introduced in order to compare it with the standard instrumentation and with Per-io-Tor in extracted teeth. Per-io-Tor and the mentioned sonic instrument seemed to be adequate for soft deposits´ elimination in the root surface, but not for calculus removal [15].
Another study compared in vivo the effect of two piezoelectric US, Vector scaler and Enac scaler, with a hand scaler. Instrumentation was completed until the obtaining of a hard surface. Roughness, amount of remaining calculus and loss of dental substance were examined by SEM. Vectorial US provided a smooth root surface with minimal dental substance loss [16].
EMS piezoelectric US Piezon Master
The effects of US were described in 1969 by Clark [17]: they depend on the vibratory movement amplitude, the pressure applied, the instrument´s tip sharpness, and the tip´s application angle and time by surface unit. Their effects condition the way of use: they should be used at 40-50% of their power to avoid the metal fatigue and to favour the long-term duration of device and tip, they should be applied tangentially (parallel to the root surface) to avoid damage in the cementum surface (Figure 9), they should never be applied with the tip perpendicular to the cementum and the tip should be in a continuous movement (Figure 10) in order to avoid the production of holes in enamel and cementum. To avoid an excessive increase of temperature, the irrigation should be abundant (Figure 11), and to achieve an optimal efficacy the most suitable tip should be selected for each indication. It should be taken into account that it is different to work over a thick layer of supragingival calculus than over a thin subgingival layer, which is more adhered. This is the reason why large tips are used for superficial calculus, small tips for subgingival calculus, curette-like for scaling and thin and long for narrow and deep pockets (Figure 11).
Hole in cementum due to a wrong ultrasonic instrumentation. Original magnification x600
Insert application and displacement for calculus removal
Supra (left) and thin subgingival (right) ultrasonic tips should always work with abundant irrigation
When US are used with complementary water tank and an antiseptic liquid, it is convenient to wash the whole circuit with demineralized water after its use, so the obstruction of tubes with the substances used is avoided. In case of using only water, it is recommended to fill in the deposit with low mineralized water, in order to facilitate the cleaning and prevent obstructions in tubes and inserts.
Due to their lineal oscillation over the dental surface, the actual rounded-tip piezoelectric US, reduce abrasion and obtain a uniform and smooth surface. With 32.000 oscillations per second, they are autoregulated and their cavitation effect and acoustic streaming reduce discomfort and have limited effects over gingival epithelium (Figure 12).
Vector decomposition of ultrasonic oscillation
Some of these US may incorporate two bottles, one for the bactericidal agent and the other for water for clearing or cleaning. They are also equipped with perio and endodontic tips.
Ultrasounds present few contraindications. They are not recommended in children except in very concrete cases. They should be avoided in the proximity of composite resins, as they could produce roughness or even detachment of the filling. They should not be used directly over ceramic partial fixed prosthesis or veneers, as ceramic could detach or break. In patients with certain types of pacemakers, interferences could be produced with inhibition and increase of the stimulation frequency. It is recommended the intermittent use of ultrasounds, avoiding the support of instruments over the generator as well as deprogramming the frequency modulation during the sessions. With a magnet, the pacemaker, which usually works at demand mode, converts into fixed-rate, not being sensitive to electromagnetic fields. In case of non sensible to electromagnetic interferences pacemakers, US could be used in the same way as in patients without pacemakers. Another option in these patients is the use of sonicators (Figure 13) because they use an air flow so they don´t generate electromagnetic fields.
Sonicator and varied tips
These instruments present certain advantages and disadvantages in relation to ultrasounds. Their oscillation frequency is much lower, of 2,000 Hz, because the oscillation is produced by the air that arrives directly from the equipment and generates an orbital oscillation in the application tip. Their efficacy is similar to that of ultrasounds, but they can only use water instead of antiseptic liquids and the set of tips is much more reduced than the ultrasounds.
Ultrasounds are used as preventive and complementary to surgery treatment in implants. In this case the tip should not be metallic but of teflon, in order to avoid the damage of the implants´ surface (Figure 14).
EMS Teflon insert for implants´ instrumentation
Fox et al. compared plastic and metal curettes in titanium implants in an in vitro study. Plastic instruments produced an insignificant alteration of the implants´ surface after instrumentation, in contrast with metal instruments, which significantly altered this surface [18].
Something similar occurs when using Piezoelectric Ultrasonic Scalers with carbon, plastic and metallic tips on titanium implants. Remaining plaque and calculus index seemed to be similar with the three treatments. When using a laser profilometer and a laser scanning electron microscope to evaluate the treated abutment surface characteristics, implants treated with carbon and plastic tips presented smoother surfaces than those treated with metallic tips, which were more damaged [19].
US were incorporated into this field in 1957 when Richman used them for root canal cleaning and instrumentation [20]. In 1976, Martin improved endodontic treatment adding simultaneous irrigation, but its commercialization and use only were extended from 1980 by Martin et al. [21]. There are sonic apparatus in which special files are used, and several ultrasonic devices which work with standard files, with the usual colours and diameters (Figure 15).
EMS ultrasonic handle and several endodontic K-files.
In endodontics US work by a transversal vibration, with a characteristic pattern of nodes and antinodes along the file´s length (Figure 16) [22, 23], and may work in two different ways: with simultaneous ultrasonic instrumentation and irrigation (UI) or with passive ultrasonic irrigation (PUI), which works in an alternating way.
Diagrammatic representation of the current observed in ultrasonic (A) and sonic (B) activated files [24].
As for ultrasonic instrumentation UI, it is discussed if the root canals thus instrumented are significantly cleaner than those prepared with files in the usual way. Some authors support UI cleaning is better [25-29], while other studies affirm the cleaning is similar [30-36]. For Ruddle, these differences could be due to the limited space available in the root canal to let the ultrasonic vibration [37]. Also the lack of space could be responsible of the lesions produced during ultrasonic instrumentation, such as perforations and deficient root canal preparations [38]. This is the reason why this technique is only recommended after the complete root canal preparation [39], by what is known as PUI.
Passive ultrasonic irrigation was described by Weller [40] as a technique in which the effect of the ultrasonic tip reduces the risk of contact with the root canal surface, thus reducing the risk of perforation, while the cavitation and cleaning effects are preserved. As the root canal has already been prepared, the file moves freely and the irrigant penetrates easily in the apical area of the root canal system [41]. In this technique two ways of irrigation may be used: continuous or discontinuous, in which irrigation works intermittently after each ultrasonic cycle. Both of them allow control of irrigation, so they seem to be equally efficient [42].
Sonic instruments may also be used for root canal therapy with similar results. Jensen et al. compare the sonic and ultrasonic cleaning efficacy after manual instrumentation in molars with curved roots. Results are analysed with photomicrographs with a grid in order to quantify the debris and evaluate the root canal cleaning level in the three groups. Sonic and ultrasonic treated molars after manual instrumentation seemed to be cleaner than those only manually treated, while the level of cleaning among sonic and ultrasonically treated molars was similar [43].
Another recent in vitro study compares the ability of different ultrasound irrigation procedures to eliminate debris and to open the dentine tubules. Previously instrumented with mechanical rotatory technique single-rooted extracted teeth are treated with US. The amount of debris and the number of open dentinal tubules were established by SEM. In the apical third, ultrasonic activation of the irrigation with Irrisafe tips seemed to be the most effective method to eliminate debris and open dentinal tubules [44].
According to Martí-Bowen et al., the use of US in periapical surgery with retrograde filling, it is feasible to reach difficult access root canals with sacrifice of few root tissue. Nowadays, good results are obtained in teeth with periapical pathology which previously were condemned to failure [45].
Van der Sluis et al. summarize the potential uses of US in endodontics with the following options: to improve the endodontic access (for example elimination of calcifications), irrigation of root canals, to remove broken posts and other obstructions inside the root canals, humectation with sealer of the root canal walls, guttapercha condensation of the obturations of root canals, mineral trioxide aggregate (MTA) application, endodontic surgery, and increase of the dentinal permeability in dental bleaching [46]; also to break fillings due to their shock effect, to remove old fillings and make easier the access to root canals, and in endodontic retreatments. There are available different applicators with the most adequate form for each use (Figures 17, 18).
Satelec EndoSuccess Retreatment Kit. From left to right, tips for dentinal overhangs, calcificatons or filling materials elimination; for treatments in the coronal third; for treatments in the medium and apical thirds; for retreatment in coronal third and isthmus; for canal probing; and for loosening of posts and crowns. (Courtesy of Satelec, Merignac Cedex, France)
Satelec EndoSuccess Apical Surgery Kit. From left to right, universal apical surgery tip; second instrument; complicated cases (up to the coronal third), premolar left-orientated tip; premolar right orientated tip. (Courtesy of Satelec, Merignac Cedex, France)
Another application of US in dentistry is in oral and maxillofacial surgery to cut hard tissues. Experimental studies show that their application present better histological results than the rotary techniques. The precision of the cut with the different available inserts allows their use in our specialization in different fields such as general oral surgery, osseous grafts and implantology.
Although initially their use was reduced to sinus lift procedures, because they preserve the sinus membrane, their use has been extended to obtain bone grafts, osseous distraction and cortical split procedures, inferior dental nerve surgery, implant surgeries, extractions, etc. These biophotonic equipments allow changes in vibration\'s frequency from standard mode, with constant vibrations and frequency (used over soft tissues), to surgery mode (for hard tissues), where the modulation of amplitude and continuous vibration improves the efficacy over bone. Several applicators are designed for each osseous intervention (Figure 19).
EMS Piezon Master Surgery US presents tips (from left to right) for vertical non-traumatic osseous incision, horizontal non-traumatic osseous incision, non-traumatic osteotomy, detachment of Schneider´s membrane during sinus lift procedures and obtaining of bone fragments for bone augmentation.
The tips are different depending on the application: they present multiple lateral impact for surgery; curved, thin and scalpel-like for osteotomy; thin for non-traumatic extractions; cone-shaped diamond covered and calibrated for guiding during preparation; rounded or flat, diamond covered or scaler-shaped for sinus lift procedures. There are multiple surgical possibilities, as it is possible to do thin incisions for grafts, cysts elimination, sinus lift procedures with alveolar or lateral access, extractions, osteoplasties, osteotomies and other.
The advantages justifying their use are less bleeding and thus better visibility during the intervention, higher cut precision than with traditional instruments and less increase of temperature, less discomfort for patients as ultrasonic vibration is less noisy than drilling, and especially that the action over the soft tissues is minimal when they are accidentally applied over them, without tearing them up.
Mectron Piezosurgery´s basic surgery and sinus lift procedure kits.
The action of the tip is effectuated by two mechanical effects: direct and indirect. In the direct mechanical effect, the tissues in contact with the tip are under a very high frequency. It is the effect of a hammer working only over the hard tissues. In the indirect one, positive and negative pressures are generated over the fluids; they are known as cavitation, and they displace the osseous tissue and potentiate the mechanical effects. This produces localized osseous destruction in a continuous or discontinuous way, being the surgeon who decides one or another possibility depending on the osseous density and the required refrigeration. This makes the cut selective without neither microscopic osseous nor soft tissue alterations. Refrigeration should be abundant with saline solution, in order to avoid heating and wash up the field to obtain a better vision.
Kits are usually available for each type of indication. The insert size and angulation allow the use depending on the necessities of the case. There are basic kits, kits for surgery, osseous distraction, implants, endodontic surgery, alveolar and lateral sinus lift procedures, osteoplasy and ostectomy, etc (Figure 20).
US trays deserve to be mentioned. Their utilization is essential in the dental office as intermediate step between the washing with soap and the sterilization of instrumental. They allow the elimination of organic debris that remain adhered in the instrument gaps facilitating the sterilization (Figure 21).
US tray.
Other applications of ultrasounds in Dentistry are removal of broken screws in implants, posts and crowns removal, etc. (Figure 22), but these applications are less frequent, they are not standardized and each professional acts according to his guidelines.
Set of diverse US tips
The evolution of US in dentistry during the last 65 years has been revised. The first laminated devices, only used for supragingival and slightly subgingival tartrectomies, have lead to sonicators and newer piezoelectric US with multiple inserts which allow the performance of tartrectomies reducing patient´s discomfort and subgingival instrumentation. The variety of available tips lets us choose those which better adapt to our necessities and to the clinical situation, even in cases of periimplantitis. In endodontics, tips to facilitate the access, to clean the root canal and to carry out retreatments are available.
The industry offers the clinician optimal possibilities to achieve retrograde fillings more difficult or even impossible to carry out with other techniques. Among the latest applications, new possibilities emerge to effectuate certain surgical treatments, sinus lift procedures, implants placement, removal of fillings and crowns and other clinical situations.
Taking into account the great advance in US technology during the last years, it is reasonable to anticipate a great future for these devices. We are commited to regularly revisit the literature in order to know new opportunities provided by technology so the most suitable device is used in each clinical situation.
In 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, RF, RS, AdaBoost, Stacking, and Voting | NASA datasets: CM1 JM1 KC1 KC2 KC3 KC4 MC1 MC2 MW1 PC1 PC2 PC3 PC4 PC5 | 10-fold CV, ACC, and AUC Vote 88.48% random forest 87.90% |
[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, AdaBoost.NC | 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, Simple Logistic Clustering: KM, EM Rule based: Ripper, Ridor NNs: RBF Nearest neighbor: KNN DTs: J48, LMT | 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 | APE system combines seven classifiers: SGD, weighted SVMs (W-SVMs), LR, MNB and Bernoulli naive Bayes (BNB) | 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, NB, BN, MLP, RBF SVM, VP, CART, J48, ADT, Nnge, DTNB | Bagging, random forest, Logistic model trees, Logit Boost, Ada Boost | 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:
“A pattern is an idea that has been useful in one practical context and will probably be useful in others.” [84]
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 | |
Android Git | Defect Pred. | Android version bug reports | |
PROMISE | Defect Pred. Effort Est. | It includes 20 datasets for defect prediction and cost estimation | |
Software Defect Pred. Data | Defect Pred. | It includes software metrics, # of defects, etc. Eclipse JDT: Eclipse PDE: | |
PMART | Design pattern mining | It has 22 patterns 9 Projects, 139 ins. Format: XML Manually detected and validated |
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 |
"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges".
\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.
",metaTitle:"About Open Access",metaDescription:"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges.\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.",metaKeywords:null,canonicalURL:"about-open-access",contentRaw:'[{"type":"htmlEditorComponent","content":"The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
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\\n"}]'},components:[{type:"htmlEditorComponent",content:'The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\nOAI-PMH
\n\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\n\nLicense
\n\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
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\n\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\n\nDigital Archiving Policy
\n\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
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