| 61. |
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| 62. |
- Afzal, Wasif, et al.
(författare)
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A systematic review of search-based testing for non-functional system properties
- 2009
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Ingår i: Information and Software Technology. - : Butterworth-Heinemann Newton, MA, USA. - 0950-5849 .- 1873-6025. ; 51:6, s. 957-976
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Tidskriftsartikel (refereegranskat)abstract
- Search-based software testing is the application of metaheuristic search techniques to generate software tests. The test adequacy criterion is transformed into a fitness function and a set of solutions in the search space are evaluated with respect to the fitness function using a metaheuristic search technique. The application of metaheuristic search techniques for testing is promising due to the fact that exhaustive testing is infeasible considering the size and complexity of software under test. Search-based software testing has been applied across the spectrum of test case design methods; this includes white-box (structural), black-box (functional) and grey-box (combination of structural and functional) testing. In addition, metaheuristic search techniques have also been applied to test non-functional properties. The overall objective of undertaking this systematic review is to examine existing work into non-functional search-based software testing (NFSBST). We are interested in types of non-functional testing targeted using metaheuristic search techniques, different fitness functions used in different types of search-based non-functional testing and challenges in the application of these techniques. The systematic review is based on a comprehensive set of 35 articles obtained after a multi-stage selection process and have been published in the time span 1996-2007. The results of the review show that metaheuristic search techniques have been applied for non-functional testing of execution time, quality of service, security, usability and safety. A variety of metaheuristic search techniques are found to be applicable for non-functional testing including simulated annealing, tabu search, genetic algorithms, ant colony methods, grammatical evolution, genetic programming (and its variants including linear genetic programming) and swarm intelligence methods. The review reports on different fitness functions used to guide the search for each of the categories of execution time, safety, usability, quality of service and security; along with a discussion of possible challenges in the application of metaheuristic search techniques.
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| 63. |
- Afzal, Wasif, et al.
(författare)
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An experiment on the effectiveness and efficiency of exploratory testing
- 2015
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Ingår i: Empirical Software Engineering. - : Springer. - 1382-3256 .- 1573-7616. ; 20:3, s. 844-878
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Tidskriftsartikel (refereegranskat)abstract
- The exploratory testing (ET) approach is commonly applied in industry, but lacks scientific research. The scientific community needs quantitative results on the performance of ET taken from realistic experimental settings. The objective of this paper is to quantify the effectiveness and efficiency of ET vs. testing with documented test cases (test case based testing, TCT). We performed four controlled experiments where a total of 24 practitioners and 46 students performed manual functional testing using ET and TCT. We measured the number of identified defects in the 90-minute testing sessions, the detection difficulty, severity and types of the detected defects, and the number of false defect reports. The results show that ET found a significantly greater number of defects. ET also found significantly more defects of varying levels of difficulty, types and severity levels. However, the two testing approaches did not differ significantly in terms of the number of false defect reports submitted. We conclude that ET was more efficient than TCT in our experiment. ET was also more effective than TCT when detection difficulty, type of defects and severity levels are considered. The two approaches are comparable when it comes to the number of false defect reports submitted.
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| 64. |
- Afzal, Wasif, et al.
(författare)
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Genetic programming for cross-release fault count predictions in large and complex software projects
- 2010
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Ingår i: Evolutionary Computation and Optimization Algorithms in Software Engineering. - : IGI Global, Hershey, USA. - 9781615208098
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Bokkapitel (refereegranskat)abstract
- Software fault prediction can play an important role in ensuring software quality through efficient resource allocation. This could, in turn, reduce the potentially high consequential costs due to faults. Predicting faults might be even more important with the emergence of short-timed and multiple software releases aimed at quick delivery of functionality. Previous research in software fault prediction has indicated that there is a need i) to improve the validity of results by having comparisons among number of data sets from a variety of software, ii) to use appropriate model evaluation measures and iii) to use statistical testing procedures. Moreover, cross-release prediction of faults has not yet achieved sufficient attention in the literature. In an attempt to address these concerns, this paper compares the quantitative and qualitative attributes of 7 traditional and machine-learning techniques for modeling the cross-release prediction of fault count data. The comparison is done using extensive data sets gathered from a total of 7 multi-release open-source and industrial software projects. These software projects together have several years of development and are from diverse application areas, ranging from a web browser to a robotic controller software. Our quantitative analysis suggests that genetic programming (GP) tends to have better consistency in terms of goodness of fit and accuracy across majority of data sets. It also has comparatively less model bias. Qualitatively, ease of configuration and complexity are less strong points for GP even though it shows generality and gives transparent models. Artificial neural networks did not perform as well as expected while linear regression gave average predictions in terms of goodness of fit and accuracy. Support vector machine regression and traditional software reliability growth models performed below average on most of the quantitative evaluation criteria while remained on average for most of the qualitative measures.
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| 65. |
- Afzal, Wasif, et al.
(författare)
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Incorporating Metrics in an Organizational Test Strategy
- 2008
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Ingår i: International Conference on Software Testing, Verification and Validation. - : IEEE. - 9780769533889 ; , s. 304-315
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Konferensbidrag (refereegranskat)abstract
- An organizational level test strategy needs to incorporate metrics to make the testing activities visible and available to process improvements. The majority of testing measurements that are done are based on faults found in the test execution phase. In contrast, this paper investigates metrics to support software test planning and test design processes. We have assembled metrics in these two process types to support management in carrying out evidence-based test process improvements and to incorporate suitable metrics as part of an organization level test strategy. The study is composed of two steps. The first step creates a relevant context by analyzing key phases in the software testing lifecycle, while the second step identifies the attributes of software test planning and test design processes along with metric(s) support for each of the identified attributes.
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| 66. |
- Afzal, Wasif, et al.
(författare)
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Lessons from applying experimentation in software engineering prediction systems
- 2008
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Ingår i: Proceedings of The 2nd International workshop on Software Productivity Analysis and Cost Estimation (SPACE'08), Collocated with 15th Asia-Pacific Software Engineering Conference. - Beijing : State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences.
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Konferensbidrag (refereegranskat)abstract
- Within software engineering prediction systems, experiments are undertaken primarliy to investigate relationships and to measure/compare models' accuracy. This paper discusses our experience and presents useful lessons/guidelines in experimenting with software engineering prediction systems. For this purpose, we use a typical software engineering experimentation process as a baseline. We found that the typical software engineering experimentation process in software engineering is supportive in developing prediction systems and have highlighted issues more central to the domain of software engineering prediction systems.
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| 67. |
- Afzal, Wasif, et al.
(författare)
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On the application of genetic programming for software engineering predictive modeling : A systematic review
- 2011
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Ingår i: Expert Systems with Applications. - : Pergamon-Elsevier Science Ltd. - 0957-4174 .- 1873-6793. ; 38:9, s. 11984-11997
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Forskningsöversikt (refereegranskat)abstract
- The objective of this paper is to investigate the evidence for symbolic regression using genetic programming (GP) being an effective method for prediction and estimation in software engineering, when compared with regression/machine learning models and other comparison groups (including comparisons with different improvements over the standard GP algorithm). We performed a systematic review of literature that compared genetic programming models with comparative techniques based on different independent project variables. A total of 23 primary studies were obtained after searching different information sources in the time span 1995-2008. The results of the review show that symbolic regression using genetic programming has been applied in three domains within software engineering predictive modeling: (i) Software quality classification (eight primary studies). (ii) Software cost/effort/size estimation (seven primary studies). (iii) Software fault prediction/software reliability growth modeling (eight primary studies). While there is evidence in support of using genetic programming for software quality classification, software fault prediction and software reliability growth modeling: the results are inconclusive for software cost/effort/size estimation.
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| 68. |
- Afzal, Wasif, et al.
(författare)
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Prediction of fault count data using genetic programming
- 2008
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Ingår i: IEEE INMIC 2008: 12th IEEE International Multitopic Conference - Conference Proceedings. - Karachi, Pakistan : IEEE. ; , s. 349-356
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Konferensbidrag (refereegranskat)abstract
- Software reliability growth modeling helps in deciding project release time and managing project resources. A large number of such models have been presented in the past. Due to the existence of many models, the models' inherent complexity, and their accompanying assumptions; the selection of suitable models becomes a challenging task. This paper presents empirical results of using genetic programming (GP) for modeling software reliability growth based on weekly fault count data of three different industrial projects. The goodness of fit (adaptability) and predictive accuracy of the evolved model is measured using five different measures in an attempt to present a fair evaluation. The results show that the GP evolved model has statistically significant goodness of fit and predictive accuracy.
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| 69. |
- Afzal, Wasif, et al.
(författare)
-
Resampling Methods in Software Quality Classification
- 2012
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Ingår i: International Journal of Software Engineering and Knowledge Engineering. - Sweden : World Scientific. - 0218-1940. ; 22:2, s. 203-223
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Tidskriftsartikel (refereegranskat)abstract
- In the presence of a number of algorithms for classification and prediction in software engineering, there is a need to have a systematic way of assessing their performances. The performance assessment is typically done by some form of partitioning or resampling of the original data to alleviate biased estimation. For predictive and classification studies in software engineering, there is a lack of a definitive advice on the most appropriate resampling method to use. This is seen as one of the contributing factors for not being able to draw general conclusions on what modeling technique or set of predictor variables are the most appropriate. Furthermore, the use of a variety of resampling methods make it impossible to perform any formal meta-analysis of the primary study results. Therefore, it is desirable to examine the influence of various resampling methods and to quantify possible differences. Objective and method: This study empirically compares five common resampling methods (hold-out validation, repeated random sub-sampling, 10-fold cross-validation, leave-one-out cross-validation and non-parametric bootstrapping) using 8 publicly available data sets with genetic programming (GP) and multiple linear regression (MLR) as software quality classification approaches. Location of (PF, PD) pairs in the ROC (receiver operating characteristics) space and area under an ROC curve (AUC) are used as accuracy indicators. Results: The results show that in terms of the location of (PF, PD) pairs in the ROC space, bootstrapping results are in the preferred region for 3 of the 8 data sets for GP and for 4 of the 8 data sets for MLR. Based on the AUC measure, there are no significant differences between the different resampling methods using GP and MLR. Conclusion: There can be certain data set properties responsible for insignificant differences between the resampling methods based on AUC. These include imbalanced data sets, insignificant predictor variables and high-dimensional data sets. With the current selection of data sets and classification techniques, bootstrapping is a preferred method based on the location of (PF, PD) pair data in the ROC space. Hold-out validation is not a good choice for comparatively smaller data sets, where leave-one-out cross-validation (LOOCV) performs better. For comparatively larger data sets, 10-fold cross-validation performs better than LOOCV.
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| 70. |
- Afzal, Wasif
(författare)
-
Search-based approaches to software fault prediction and software testing
- 2009
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Software verification and validation activities are essential for software quality but also constitute a large part of software development costs. Therefore efficient and cost-effective software verification and validation activities are both a priority and a necessity considering the pressure to decrease time-to-market and intense competition faced by many, if not all, companies today. It is then perhaps not unexpected that decisions related to software quality, when to stop testing, testing schedule and testing resource allocation needs to be as accurate as possible. This thesis investigates the application of search-based techniques within two activities of software verification and validation: Software fault prediction and software testing for non-functional system properties. Software fault prediction modeling can provide support for making important decisions as outlined above. In this thesis we empirically evaluate symbolic regression using genetic programming (a search-based technique) as a potential method for software fault predictions. Using data sets from both industrial and open-source software, the strengths and weaknesses of applying symbolic regression in genetic programming are evaluated against competitive techniques. In addition to software fault prediction this thesis also consolidates available research into predictive modeling of other attributes by applying symbolic regression in genetic programming, thus presenting a broader perspective. As an extension to the application of search-based techniques within software verification and validation this thesis further investigates the extent of application of search-based techniques for testing non-functional system properties. Based on the research findings in this thesis it can be concluded that applying symbolic regression in genetic programming may be a viable technique for software fault prediction. We additionally seek literature evidence where other search-based techniques are applied for testing of non-functional system properties, hence contributing towards the growing application of search-based techniques in diverse activities within software verification and validation.
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