The t-test values significancy - comparing cough with breathing activities fulfilling experimenter’s instruction.
\r\n\t2. Animal and vegetal protein hydrolysates
\r\n\t3. Macroalgae seaweeds extracts
\r\n\t4. Beneficial microorganisms, etc.
\r\n\tThe elucidation of the agricultural function (i.e. improving nutrient use efficiency, quality, and tolerance to abiotic stresses) and action mechanisms of PBs will permit to develop a second generation of PBs where synergies and complementary mechanisms can be functionally designed to feed the future.
",isbn:"978-1-80355-553-9",printIsbn:"978-1-80355-552-2",pdfIsbn:"978-1-80355-554-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,hash:"4c1b31fff4d04b36466a40927904f210",bookSignature:"Dr. Vijay Singh Meena, Dr. Hanuman Prasad Parewa and Dr. Sunita Kumari Meena",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11327.jpg",keywords:"Ecological Diversity, Mutualistic Symbiosis, Temperatures, Frost, Salinity, Acidity, Nutrients, Water, Crop Yield, Grain, Straw, Biofortified",numberOfDownloads:88,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 21st 2021",dateEndSecondStepPublish:"December 1st 2021",dateEndThirdStepPublish:"January 30th 2022",dateEndFourthStepPublish:"April 20th 2022",dateEndFifthStepPublish:"June 19th 2022",remainingDaysToSecondStep:"6 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Dr. Vijay Singh Meena has worked in various aspects of soil aggregation, carbon management index as well as carbon and nitrogen sequestration potential under climate-resilient agriculture. He identified the carbon management index as the key indicator to measure soil degradation in different agro-ecosystems. He has edited 10 books on microbes, organic farming, and agricultural sustainability and received several scholarships and awards during his academic and professional career.",coeditorOneBiosketch:"Dr. Hanuman Prasad Parewa has specialized in the field of soil fertility, INM, and plant growth-promoting rhizobacteria. His research revealed that integrated application of fertilizer, FYM and bio inoculants highly effective for sustainable wheat and Mung bean production and soil quality.",coeditorTwoBiosketch:"Dr. Sunita Kumari Meena has previously served as a research scholar at both Banaras Hindu University, Varanasi, and at ICAR-Indian Agricultural Research Institute, New Delhi. She has published in the Journal of Cleaner Production, Ecological Engineering, Scientia Horticulturae, Environmental Science and Pollution Research and Biocatalysis and Agricultural Biotechnology.",coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"350226",title:"Dr.",name:"Vijay",middleName:"Singh",surname:"Meena",slug:"vijay-meena",fullName:"Vijay Meena",profilePictureURL:"https://mts.intechopen.com/storage/users/350226/images/system/350226.png",biography:"Vijay Singh Meena is currently working as a Project Coordinator (Soil Scientist) at CIMMYT-BISA, Pusa, India. His previous position includes that of Scientist (Soils) at the Indian Council of Agricultural Research (Indian equivalent to USDA-ARS). He has worked in various aspects of soil aggregation, carbon management index and carbon and nitrogen sequestration potential under climate resilient agriculture. He identified carbon management index as the key indicator to measure soil degradation in different agro-ecosystems. His research revealed that the application of FYM and vermicompost along with vegetative barrier across the slope are highly effective in sustaining the soil quality. Dr. Meena and his team identified that the combined application of organic and inorganic sources is important in sustaining the productivity of soils and prevent soil erosion. He is instrumental in the preparation and distribution of > 4000 soil health cards to different farmers. Dr. Meena and his team reported that the long-term judicious application of organic and mineral fertilizer positively influenced soil aggregation, carbon distribution, water stable aggregates, maize and wheat yields and reduced the runoff and soil loss (without fertilization) and recommended the dose of fertilizers. The concept of CMI was found to be effective in assessing the best nutrient management practices in sloppy crop lands as it showed significant correlation with yield, runoff and soil loss. The estimation of CMI values of any fertilization management can indicate the soil degradation status quantitatively thus helpful in mitigating land degradation in the hilly agro-ecosystem.\nHe also edited seven Springer books and three Elsevier books on microbes, organic farming and agricultural sustainability. He has received several scholarship and awards during his academic and professional career. 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He was awarded the UGC scholarship and Rajeev Gandhi National Fellowship for his Ph. D. programme in Soil Science & Agriculture Chemistry. He has 8 years of experience in undergraduate and post graduate teaching and research in the field of soil science and more than 2 years of experience in extension and administration. He has delivered 10 TV talk and 8 radio talks for the benefit of the farming community. He has published 2 books, 5 practical manuals, various papers in national and international journals, book chapters, and folder. He has specialized in the field of soil fertility, INM and PGPR. His research revealed that integrated application of fertilizer, FYM and bioinoculants highly effective for sustainable wheat and Mung bean production and soil quality. He has awarded many times in national and international seminars for best oral and poster presentation. 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A pressure vaginal probe was connected to a manometer to measure the intravaginal pressure induced by the contraction of the pelvic floor muscles in mmHg. Since then, pressure probes of various shapes and technical properties have been developed [2, 3]. One type of instrument used under standard conditions with a well-guided protocol is very useful for both objectifying the diagnostics and assessing the effect of therapy [4].
The pelvic floor performs two types of activities - tonic and phasic [5, 6], as confirmed by the Deindl study [7]. According to Frawley [3], manometric measurement is one of the most widespread and the advantage is that it allows the measurement of muscle contraction both when lying and standing. This advantage is that incontinence occurs especially in upright positions and therefore examination in these positions will give us information with a higher informative value. It appears that the resting pressure measurement (tonic pressure) is not as reliable in standing (ICC 0.29) and sitting position as in lying (ICC 0.77). Measurement of the pressure of phasic compression of pelvic floor muscle shows good ICC confidence of 0.91 to 0.95 in all body positions [3].
Junginger et al. [8] controlled the decline of the uterine throat and the descent of pelvic organs while increasing intraabdominal pressure, which was prevented by joint activity of the pelvic floor muscles and the transversus abdominis muscles. Iacobellis et al. also conducted a detailed study of the risk of organ descent by MR [9]. Comparing the lying and sitting situation, the difference proved to be statistically significant. This suggests that examination in lying position can underestimate the existing descent of organs.
Bø et al. [10] also ask the question whether a usual manometric examination of the pelvic floor in a lying position is sufficient or whether it is more appropriate to perform examination in a standing position. For standing persons, it has been proved that the pressure increase during will-induced contraction and dwell time have not changed, only the minimum value (tonic contraction) when standing has increased.
The pelvic floor is given responsibility for the continence of urine, stool and the supporting function of the pelvic organs. The connection between the function of the pelvic floor and respiration is rarely considered. Therefore, we wanted to contribute to the knowledge of these connections. In addition, we were interested in the difference in the functional context in the position of the body in a standing and lying position. We performed the measurements even after a minute-long run, which triggered the spontaneous deep breathing process.
During the actual measurement, the person was placed on the examination bed in a position with the legs bent and with the feet propped up, and a condom-protected perionometer probe was inserted. Subsequently, a trained therapist gave verbal instructions and checked the performance. In order to ensure all the required conditions and to eventually register an individual proband response, a second specialist therapist was present during the testing, as recommended by Bø [10], to ensure standardisation of the examination. The individual test manoeuvres were based on a commonly performed PERFECT SCALE examination, which is primarily designed for palpation vaginal examination, but allows the same procedure to be performed via a pressure probe and thus objectifying the results [11].
Maximum contraction and endurance at this level were tested for 10 seconds with subsequent relaxation. In addition, the test subject was asked to repeat maximum contraction and release with a duration of five seconds per phase. Subsequently, a similar situation was tested, but with a shorter duration of individual phases - 1 s contraction and 1 s relaxation. Finally, the person was asked to cough, which was repeated three times. See Figure 1.
Subtraction of parameters from measured data - tonic basis of muscle activity (value B). Increase in pressure by A-value in phasic muscular activity (here at the therapist’s instruction to repeat will-induced contraction of 5 s and 1 s of pelvic floor muscles and in coughing - three reps).
The second observed phenomenon was the effect of respiration, each examination lasted one minute. We compared the activity of pelvic floor muscles with calm and deep breathing without will-induced activation of pelvic floor muscles. During deep breathing, the proband was instructed to take several deeper inhalations (3 s) and exhalations (6 s). Inhale was done through the nose, exhale through the mouth using the “S” spoken throughout the exhalation. Then the proband switched to the KETTLER TRACK 3 treadmill with the probe installed and ran for a minute on this belt with a 0% slope, i.e. flat and at a speed of 5 km/h. Subsequently, we observed changes in pelvic floor muscle pressures during spontaneous breathing after a minute of running on a treadmill.
To this measurement project were involved 10 women. Their average age was 38 years (range 25–47 years), average height 166.8 cm (range 159–178 cm), average weight 66.9 kg (range 50–85 kg), average BMI 23.9 kg/m2 (range 19.0–29.8 kg/m2). There were no births in 3 women, one birth was performed by one woman, two and three births each by two women, and only one woman had 4 births. Only probands who excluded respiratory diseases, abdominal or gynaecological operations, except births, lumbar spine pain, were included in the measurement. No proband is an active athlete.
All statistical calculations were performed using the software OriginPro 8.5.1 (http://www.originlab.com/Origin). T-tests were used to test hypotheses to determine if there was a significant difference between the averages of the two groups of measurements under the given test conditions. Spearman’s rank correlation coefficients were calculated to evaluate the associations between variables. The significant at an alpha level of 0.025 (at least) or it is specified in the Results chapter for the specific situations.
The achieved increase in perionatal pressure (amplitude) was to be maintained for 10 seconds. This was ideally achieved by one person only. Six persons experienced a gradual decrease in the achieved maximum value by an average of 45% (values 31 to 78%).
As expected, a statistically significant difference can be demonstrated for the tonic activity of the pelvic floor muscles in the lying and standing positions, on average the standing values are 7.5 to 10 mm Hg higher (Figure 2). This result fully corresponds to the research of Bø et al. (Bø & Finckenhagen, 2003).
The tonic pelvic floor muscles activity in specific body position (arithmetic means and determinative deviations).
The results are not conclusive for the phasic activity, the amplitude increased statistically significantly only for cough (Table 1). The amplitudes of the 1 s and 5 s tests were comparable. Basal tonic pressure values - especially the critically low values observed in three individuals - do not condition the low values achieved in short-term amplitudes.
Comparison lying-standing body position | ||||
---|---|---|---|---|
Cough | 2,271 | 0,025 | 3,995 | 0,002 |
Calm breathing | 3,554 | 0,003 | 5,275 | 0 |
Deep breathing | 3,636 | 0,003 | 4,531 | 0,001 |
The t-test values significancy - comparing cough with breathing activities fulfilling experimenter’s instruction.
In the test of pelvic floor muscles reaction to cough, we investigate reflex functions in contrast to will-induced contractions in previous tests (5 s and 1 s). In this case, we clearly find higher amplitudes of contractions in cough, on average, the values are doubled, for one person, the cough pressure is up to 10 times the values of the person’s deliberately induced amplitude. The t-test values are statistically significant when comparing cough with all will-induced activities at a significance level of 0.001. As expected, the difference in tonic muscle activity was not detected (Figure 2).
In an effort to understand more deeply the above-described morphological-functional interconnections and behaviour of the system during the will-induced and reflexive (or spontaneous) changes, we extended the tested functional situations. We were wondering what the functional response to deepened breathing would be, which we invoke not spontaneously but based on the instructions of the therapist. In addition, a short-term anaerobic load lasting several tens of seconds was chosen.
A statistically significant increase in pelvic floor muscle activity during deep breathing was demonstrated. Comparing this activity with consciously induced deep breathing while standing, with the spontaneous breathing caused by the previous running activity can be considered a very interesting result (Figure 3). Phasic activity of pelvic floor muscles in deep breathing is statistically significantly higher than that measured in deepened breath after physical exercise of the tested person for a minute of running on the treadmill.
Phasic muscle activity in deep breathing after running and fulfilling experimenter’s instruction.
The statistical comparison shows that the increase in the pelvic floor muscular phasic pressure due to cough differs significantly in all the situations tested, i.e. it differs in relation to the position (lying, standing) and to three types of breathing (quiet, deep, after running) (Figure 4 and Table 2).
Values of arithmetic means and determinative deviations for observed situations.
Comparison calm and deep breathing | ||
---|---|---|
Lying - calm vs. deep breathing | −2,443 | 0,019 |
Standing - calm vs. deep breathing | −3,636 | 0,003 |
Standing - calm vs. after running | −3,563 | 0,003 |
Standing – deep vs. after running | 3,209 | 0,005 |
Comparison calm and deep breathing and situation after a minute run.
While in deep breathing we find large variations of values, in spontaneous breath after a minute-run, the values are scattered minimally, similarly to calm breath in both monitored positions, which we consider an interesting result.
If we monitor the variability of the observed values within the measured group, we can see that while in deep breathing we find large variations of the values of physical pressure increase, in spontaneous breath after a minute run, the values are scattered to a minimum extent, similarly to calm breath. Everything applies to both standing and lying position (Figure 4).
The aim of our work was to assess how the degree of functional involvement of the pelvic floor changes based on breathing of the tested person, depending on the position of the body in an upright position or lying down.
Our results showed statistically significant differences in tonic activity of pelvic floor muscles in lying and standing position. At the same time, however, we found in the initial study that the will-induced - that is, the phasic muscular contractions of the pelvic floor muscles are not different when comparing measurements taken while standing or lying down (Table 3). In other words, the amplitude increase (the value of the short-term pressure increase referred to as phasic action) was approximately the same for both lying and standing position, only when lying down there was a lower level of tonic contraction than when standing (the value of sustained tonic activity).
Comparison lying-standing body position | ||
---|---|---|
Intertionally contracted | no | yes |
Deep breathing | yes | yes |
Cough | yes | yes |
Comparison of the response of the phasic and tonic activity of the pelvic floor muscles to the experimenter’s instruction (“yes” means an increase in standing activity confirmed by mathematical statistics).
Only in reflex muscle activity (i.e. cough simulation), differences were detected not only in tonic muscle activity, but also in phasic muscle activity. Thus, it can be argued that this spontaneous response of the system to a cough maintains a tendency to higher amplitudes. This happens both when lying and standing. To evaluate the associations between lying or standing activity, Spearman correlation coefficients were calculated for the same breath type (Table 4).
Spearman’s correlation - lying-standing body position | ||||
---|---|---|---|---|
Cough | 0,761 | 0,05 | ||
Calm breathing | 0,233 | no | ||
Deep breathing |
Test of the relationship between the increase of tonic and phasic activity of muscles by changing the position of the body.
Our results show that the instruction to deepen breathing led to a different increase in the phasic muscular activity of these muscles. Phasic activity of pelvic floor muscles in deep breathing is statistically significantly higher than that measured in deepened breath after physical exercise of the tested person for a minute of running on the treadmill.
The functional relationships of postural and respiratory function of the pelvic floor is known. But many clinical procedures rely on only one of these areas. It is more advantageous for the patient to undergo therapy linking the functional influence of the whole respiratory system and the pelvic floor system.
Not only in the direct treatment of pelvic floor dysfunctions, but also in the treatment of widespread low back pain. In routine clinical practice, the pelvic floor, in a patient with back pain, is not examined. If we remove all the pathologies found in the patient - muscle spasms, trigger points, joint blockages, muscle contractions, etc., without treatment of the pelvic floor, the patient will have temporary relief, but the pain will return.
The study was supported by SVV 2017-2019-260346 and PROGRES Q41.
Emerging technology breakthroughs in a number of fields, such The Internet of Things, 5G, artificial intelligence, etc., are propelling a continuous increase in the size and complexity of software in communication systems. In such applications, software must have—among others—two important characteristics: (1) quality and (2) agility. Software quality is important to limit downtime and vulnerabilities that might have unprecedented consequences for the anticipated societal-critical applications, while agility helps to continuously improve services to meet customer needs. In order to meet business objectives, and supported by advances in virtualization technologies, Telecom service providers are recently moving towards service agility, where the aim is to create new or improve the services provided to their users [1]. To this end, network software vendors must have the capability to continuously deliver and integrate software from which such services are composed.
\nThese requirements mean that development teams must be able to produce quality software in very short time periods. This calls for accurate ways of planning team resource requirements or software size (number/complexity of features) ahead of time to avoid failing to meet customer needs. A useful approach for this assessment is
Software quality can be significantly affected by the number of defects and amount/type of testing carried out at the different stages of the software development phase. In this subsection, we examine the defect flow through various development phases and in the field. Once a set of new features is identified, each of them goes through the phases shown in Figure 1. It can be observed that there are a number of activities overlapping in time (
Defect flow development, test, field (traditional vs. agile).
A proportion of defects that remain at the end of the test phase are normally encountered during operation in the field, and usually, only a fraction of them lead to failures or outages. However, an outage puts a system down, making it go through a process of respond-recover-resolve. Finally, some of the residual defects will usually not be found even during field operation. These then become base or old defects for the following release.
\nThe quality of software is measured in terms of software defects found during the customer operation period. SRGM is usually used to quantify the quality of software products before they are delivered to customers. SRGM is a very well-studied subject, with over 200 SRGMs developed since early 1970s [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]. These models can generally be categorized as either being
Parametric models are based on explicit mathematical expressions and physical interpretation. They were originally designed for system test but continue to be extended to include functional, integration, and other earlier test phases. They are the earliest developed models, are easily understood, and widely used. Most of the frequently used parametric models can be systematically grouped based on the shape of the function (i.e.
Recently, non-parametric SRGMs are being proposed. Such models typically use
This chapter discusses advances to the above techniques and approaches using BRACE [21], a cloud-based, fully-automated tool for software defect prediction, reliability and availability modeling and analytics. BRACE includes a number of technical contributions to make software defect prediction more practical for every software development project. First, the tool unifies and automates the entire process of data extraction, pre-processing, core processing and post-processing. Second, the core data analytics engine of BRACE—SRGM—provides a robust, consistent, flexible, fast, statistically sound approach to defect prediction for any defect data set without human intervention. This is achieved by modeling the entire defect trend as a series of piece-wise exponential curves, and incorporating a mechanism to automatically detect when to transition from one curve to the next. Moreover, to enhance the accuracy, whenever available, the algorithm can also take as input feature arrival data (which is a measure of development effort). This allows the enhanced SRGM to provide defect prediction from the project planning phase through the internal testing phase and the customer site test and customer operation periods. Finally, BRACE exposes a user interface which displays the output generated from the core processing. It makes it easier for projects to understand the current progress towards quality improvement. To the best of our knowledge, this is the first attempt to propose a practical software defect prediction and reliability management solution that can be re-used across multiple teams at industrial scale.
\nThe rest of this chapter is organized as follows: In Section 2, we present the design, implementation and use cases of BRACE. We also describe datasets from two example projects that are used as references throughout the chapter. In Section 3, we detail an automated SRGM algorithm which is the core analytics engine of BRACE. The algorithm enhances traditional SRGMs by enabling accurate early defect prediction, which, as mentioned earlier, is a necessity for most projects. Sections 4 and 5 will discuss key post-delivery metrics:
BRACE consists of three main processes: (1)
The pre-processing step is made up of a generic data-collector which uses application programming interfaces (APIs) to collect data from various sources. As example, defect data can be obtained from Jira while other project-related information may be obtained from GitLab. The data is generally obtained as records of defects with the corresponding fields (such as creation date, resolution date, defect type, software release, etc.). Once the data is received for project, a number of processing steps are carried out. These might include—as necessary—
The core processing implements the optimization function which derives
Finally, the post-processing step takes as input the metrics from the core processing so as to generate various types of tables and charts, depending on project’s needs. As an example, this step may be able to present outputs such as software failure rate, software availability & reliability, software annual downtime, defect rate, and predicted defects. Moreover, it also provides confidence limits for each of the calculated metrics. Therefore, the post-processing stage is aimed at providing answers (using a GUI) to the questions presented in Table 1. Answers to these use cases are the main motivations of the work done by software reliability experts, and by extension the main motivations of BRACE. As such, answers to the questions below will be provided throughout the rest of this chapter. We discuss details regarding these outputs and final processing steps in Sections 4 and 5, respectively.
BRACE main processes.
\n
| \n
BRACE use cases (motivating questions).
Defect data sets, called Projects A & B, are briefly described. Both projects represent large-scale software development from telecom products. Figure 3 shows an example of a 3G & 4G wireless network system, where projects A and B can be found in the diagram.
\nSample 3G & 4G wireless telecom network system.
This is a key wireless product, called RNC, which is responsible for the control and management of radio resources in a wireless network. It is a large-scale software development for a key 3G wireless telecommunication product with a high availability redundant hardware configuration. Code size varies from over 500 KNCSL (1000 non-commentary source lines) in earlier releases to less than 100 KNCSL as customers migrate from 3G to 4G systems. A traditional delivery scheme of one delivery per release was used. A major hardware platform change took place during the reported period, which resulted in redesigning the software architecture.
\nIt takes advantages of hardware technological improvement, by introducing additional software redundancy as well as upgrading and re-designing the hardware platform with a pair of active-active processors. The new hardware platform supports multiple copies of a key software component as many pairs of active-standby software configurations. As discussed in Section 4, this feature helped improve the capacity as well as the availability since the impact of failure software becomes very small due to a pair of active-standby. The quality impact of the major changes will be highlighted in Section 4. The data sets used in this chapter cover 11 releases over 5 years.
\nIt represents a radio access part of the latest 4G mobile network technology. The two 3G key functions were combined into one, called eNB, to meet high data rate requirements. It performs the control and management of radio resources in a wireless network, plus radio frequency transmitters and receivers used to directly communicate with mobile devices. It is built on a highly complex hardware design and sophisticated software architecture. The software development is required to deliver many complex new features to meet demand in fast growing markets. Recent releases contain over 1 million non-commentary source lines (MNCSL) new features and deploy a new delivery scheme of multiple deliveries per release to satisfy the needs for additional features by multiple customers. Similar to Project A, this project also went through a major hardware platform change and drastic software redesign. The quality impact will be shown in Section 4.
\nAs earlier mentioned, exponential models assume that defects can be found and resolved at a constant rate [10]. While this results into a simple and flexible model with well understood assumptions, it is not always the case that software development is stable, as sometimes changes have to be made to the processes. To ensure that defect prediction adapts to such trend changes, we apply the exponential prediction model to each wave of software defects. The idea of piecewise application of SRGMs is not exactly new. For example, a concept for evolving software content was originally discussed in [10]. This is the first time that we successfully formulated it mathematically, developed an innovative algorithm to automate the process and implemented it in a cloud environment.
\nThe mathematical model is a non-homogeneous Poisson Process (NHPP) with a mean value function following an exponential model. The tool uses a piece-wise application of NHPP exponential models as illustrated in Figure 4. The NHPP assumption is used to implement the statistical method of maximum likelihood for estimating model parameters with the normal approximation confidence limits for a set of defect data. As new test defect data becomes available, we continuously monitor and predict residual (or remaining) defects at delivery. It then uses the last curve for predicting defects to be found after delivery to customer site.
\nAn example of a piece-wise application of NHPP model.
To illustrate the model, consider a finite number,
In practice, the value of
Note that
The parameters
It should be pointed out that if
Answers to use cases (a), (b) & (c) in Section 2 can be illustrated using Figure 5 as follows: SRGM predicts 3700 defects by the delivery date. Therefore, assuming that current date corresponds to week 19 (vertical blue line), we would expect to find 1200 more defects in the 11 weeks to delivery assuming the same test progress continues. Since SRGM predicts a total of 4500 defects and 3700 defects at delivery, the
Example of software defect prediction.
As illustrated above, residual defects, which are derived from the defect arrival curve using SRGM, play a key role in software quality assessment in terms of delivery readiness. Our recommendation is that readiness is given as green (implying ‘good to go’) if the threshold is less than 15%, yellow if it is between 15 and 25%, and red if it is greater than 25%. In addition, it is important to track backlog defects at delivery, so as not to deliver known issues. Our recommendation is that all customer critical and major issues be resolved by delivery. We will address how to predict backlog defects in Section 3.2.2.
\nTypical SRGM techniques require defect data from the software test period. This limits their use during the early phases of software development during which it is usually necessary to make important (and time intensive) decisions (such as the level of staffing or amount of required testing or number of features to focus on) about the development process. Considering the industry trend towards very short software development lifecycles (i.e. agile development), it is essential to be able to make such decisions accurately very early on in the development phase. Specifically, in order to determine the staffing requirements for development and test activities during the early planning phase, many projects now need to understand what the defect find curve would look like during the internal test period. Therefore, early software defect prediction is needed for the early identification of software quality, cost overrun, and optimal development strategy.
\nWe propose a novel method, eDPM, for predicting defect arrival curves based on the feature arrival curve during the planning phase. The feature arrival curve often gives the number of sub-features for each feature of the project, together with the times when each sub-feature is expected to be completed. Such information is usually available during the development planning phase of the software development life cycle. Specifically, eDPM involves using data from a previous release of the same product, together with the feature arrival curve for the upcoming release. In order to produce a reliability modeling approach that covers the whole development process, the eDPM approach has been integrated into BRACE as an enhanced SRGM.
\neDPM uses two transformation functions: one horizontal shift and one vertical shift. We have performed a statistical correlation study (e.g., quantile-quantile or Q-Q plot [22]) and found a very high correlation. Figure 6 illustrates a Q-Q plot for Project B data. If the data points follow a straight line, it indicates a strong correlation between two factors being considered or statistically, the distributions of the two factors are the same. That is, both curves have similarity in shape.
\nA sample Q-Q plot for project B release 5 data.
Let (x, y) represent a feature curve and (xnew, ynew) represent a defect arrival curve. We can move the feature curve to the right and closer to the defect arrival curve with the horizontal shift function in (5)
\nwhere \n
The parameter \n
eDPM defect arrival curve prediction based on feature arrival curve.
We will now provide four case studies to demonstrate the robustness of eDPM for practical uses. It should be pointed out that the team “feature” is used here in a generic sense to represent either sub-feature, epic, story, or sprint depending on the availability of metrics for individual projects. Similarly, the term “release” represents a set of features defined for each software delivery. The release content continues to evolve over the software lifecycle. It is important to continuously monitor the release content and adjust the transformation functions to improve the prediction accuracy. While out of scope for this chapter, we have recently developed an algorithm which automates the estimation of parameters as new feature and defect data becomes available.
\neDPM case study #1: Previous release data.
eDMP case study #2: Test cases executed vs. test defects.
eDPM case study #3: Development start vs. feature ready vs. test defects.
eDPM case study #4: Story points vs. integration test defects.
It was later confirmed that there were two major process changes made during the reported period. As eDPM was applied to the data (Transformation #1), we were able to identify the trend change after several months where the transformation is no longer valid. It turns out that the trend change occurred when a major process change was made. Another set of transformation functions, called Transformation #2, were then used. The predicted values are very closely matching with actual defect data. Several months later, we encountered another trend change, which turns out to be caused by another major process change. We then used another transformation #3. With the successive use of eDPM we demonstrated that defects can be predicted with reasonable accuracy for the entire reported period.
\nAnother benefit of eDPM is to help quantify the process improvement. One of the parameters,
eDPM case study #5: sub-feature, defect arrival/closure/backlog.
In Section 3, we demonstrated that the last curve prior to software delivery represents the final product from which the total number of defects and residual defects can be calculated. Previous release data or historical data from other projects will be helpful for determining the percent of delivered defects to be found during the operation period. See [16] for detailed discussions.
\nThe assumption that the defect curve can be extended from the development phase into the operational phase (e.g. [23, 24, 25]) does not hold in practice, as there are usually discontinuities due to changes in the intensity of testing, as well as operational conditions not always being exactly the same as test environments [7].
\nTo highlight the procedure and results we will use defect data taken from Project B. Figure 13 shows a cumulative view of customer defect prediction. Note that the curve should be always above the actual data after delivery. The difference between the curve and the last actual data point indicates the defects not found in this release and they will become a part of the next release. That is, not all delivered defects will be found during the operation period. It also demonstrates that actual data follows as predicted, indicating the importance of historical data in predicting post-delivery defects.
\nCumulative view of project B customer defect prediction vs. actual.
Figure 14 illustrates the difference in defect rate, λ(
Weekly view of project B customer defect prediction vs. actual.
In recent years many product suppliers have been implementing complex software-controlled systems with a large number of software features on a short development schedule. In the telecom industry, a critical customer operational issue is on system performance, especially in terms of system outages impacting the service availability for their end users. As a result, service providers frequently ask their product suppliers for software reliability and availability measurements. In this section, we discuss the relationship between software failure rate, availability, and reliability.
\nField outage measurements are required for telecom products by TL9000 [26], which is a quality management system (QMS). It standardizes the quality system requirements for the design, development, delivery, installation, and maintenance of telecom products and services. It defines the reliability in terms of SO3 (service outage frequency) and SO4 (service outage duration) metrics. As demonstrated in [16] the defect find process during the operation period maybe modeled as a stationary Poisson process. It also follows that the rate of software failure (or outage) rate for each release can be modeled as stationary Poisson process. Consider a software release with a failure rate λ and defect rate λ
Reliability and availability and among the key factors that are used to define the quality of software in practice. In what follows, we formulate mathematical representations for both these factors.
\nThe availability of software can be expressed using cycles of uninterrupted working intervals (Uptime), followed by a repair period after a failure has occurred (Downtime) using (8).
\nConsidering that availability is typically evaluated over a 1 year period, \n
On the other hand, software
It is important to note that while both reliability and availability are a measure of software quality, they have different technical meanings. In particular, availability is determined by both uptime and downtime, while reliability is only influenced by uptime. This implies that two software releases or systems having the same failure rate, would have the same reliability, but might have different availabilities. Achieving a high availability generally requires having automated ways of recovering from failures, for example, through redundancy or rebooting, so that the downtime is minimized. Software failures for which the system is able to automatically recover are known as covered failures. On the other hand, if a system fails to automatically detect and/or recover from a failure, such a failure is known as an uncovered failure, and usually leads to customer perceived defects. In systems where recovery time is significant, a coverage factor – the proportion of all failures that are covered failures – is defined. However, in most practical applications, it requires specialized tools to determine covered failures. Therefore, typical failure counts usually only consider the uncovered defects.
\nIn what follows, we use (anonymised/scaled) data from project A to demonstrate the various aspects of software failure, reliability and availability, together with the predictions that are carried for the same. The data compares multiple releases of a software product over multiple years. Outage data represents unplanned, customer-reported, and uncovered failures, including full and partial outages. The outages were collected across a deployment of over 400 systems. The monthly outage count is annualized and normalized by the number of deployed systems as outages/year/system, which is equivalent to the failure rate. In the same way, the monthly outage downtime is annualized and normalized by deployed systems as downtime/year/deployed system. It should be noted that the downtime duration of each outage is discounted by percentage impact (i.e. 100% being a full outage), using the TL 9000 counting rule.
\nIn Figure 15, we show the predicted software reliability as a function of failure rate (on the left) and software availability as a function of annual downtime (on the right). The following observations can be made:
From one release to another, the actual data generally lies within the 90% confidence limits for both availability and reliability. This is testament to the accuracy of the generated predictions.
Over time, from one release to the next, we can observe a continuous improvement in software availability and reliability. This is not surprising since it takes time and increased effort to enhance software design, development and test practices.
There is a slight deterioration in reliability and availability at R5, corresponding to a change in hardware, but these quickly improve again after that. This can be explained by the need to re-design the software, but also demonstrates the important effect hardware can have on the quality of software, i.e. sometimes significant long term improvements in software quality may only be achieved through changes in hardware.
It is worth observing that predicting availability is generally more difficult than predicting reliability. This is due to the fact that availability is affected by the downtime while reliability is not. In addition, as software development teams get used to a product from one release to the next, they get used to the system, and therefore, are normally able to significantly reduce the average system downtime.
Release-over release software reliability and availability prediction—Project A.
Finally, we applied the method to Project B. In this project, it is not practical to collect the system downtime in the field due to the nature of the product. However, customers are concerned about resets. Therefore, the focus is on the number of unplanned autonomous resets. Figure 16 summarizes the annual reset rate with prediction and actual data over several releases. The predicted values are remarkably close to actual data and within the 90% limits. Although actual downtime is not available, we can use reset time measured in the lab to calculate the reset-based availability using the reset rate prediction.
\nSoftware failure rate prediction—Project B.
Figure 17 shows the implementation of BRACE. The tool is made up of multiple application programming interfaces (APIs), each of them connecting to a defect logging database (such as JIRA). Defect data is collected from the defect databases in real-time and pre-processed by a computer program (in Python) before being stored into a cloud-based, shared database used by the system. The SRGM algorithm (which is written in Python) then performs the core processing, providing a consistent, fast, flexible, robust, and statistically sound result. Using the output of from core processing, we have also created a unified graphical user interface (GUI) onto which a wealth of software quality metrics are presented to users. While in the current implementation all components of the tool are hosted in a virtual machine running in openstack, it is possible to have them also running in a dedicated server if needed.
\nDesign and implementation of a cloud-based BRACE.
As an example use case, for a given project, a number of input parameters are required for the tool. Such inputs include the project milestones, the require changes in defect rate before and after deployment, and a number of assumptions based on expert knowledge of both the product and development process.
\nIn this chapter we presented a practical approach to software defect prediction, which helps assure the delivery of high quality software. An innovative cloud-based analytics tool, BRACE, was introduced which automates the entire process of data extraction, pre-processing, core processing, and post-processing, combined with a user interface. It no longer relies on the use of a spreadsheet and generates prediction in real-time, which can be shared with any members of a project. SRGM is the core analytics engine which implements technical breakthroughs in this area. It provides a robust, consistent, flexible, fast, statistically sound approach to defect prediction for any defect data sets without human intervention. The enhanced version of SRGM incorporates feature arrival data to provide defect prediction throughout the lifecycle of each release with much improved accuracy. We also demonstrated the method for predicting customer defects and software availability during the operation phase, which should be the basis for software quality assurance. We demonstrated the effectiveness of the approach using data sets taken from telecom development projects, varying from traditional development to DevOps CI/CD with full agile development. This approach can be easily applied to any software development projects.
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On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. 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