Elane - Promise08
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Comparing Negative Binomial and Recursive Partitioning Models for Fault Prediction
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Nature-Based Prediction Model of Bug Reports Based on Ensemble Machine Learning Model.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.comโ
Facebook: https://www.facebook.com/pages/Shakas-TechnologiesNature-Based Prediction Model of Bug Reports Based on Ensemble Machine Learni...
Nature-Based Prediction Model of Bug Reports Based on Ensemble Machine Learni...Shakas Technologies
ย
Software Quality Assurance (SQA) teams play a critical role in the software development process to ensure the absence of software defects. It is not feasible to perform exhaustive SQA tasks (i.e., software testing and code review) on a large software product given the limited SQA resources that are available. Thus, the prioritization of SQA efforts is an essential step in all SQA efforts. Defect prediction models are used to prioritize risky software modules and understand the impact of software metrics on the defect-proneness of software modules. The predictions and insights that are derived from defect prediction models can help software teams allocate their limited SQA resources to the modules that are most likely to be defective and avoid common past pitfalls that are associated with the defective modules of the past. However, the predictions and insights that are derived from defect prediction models may be inaccurate and unreliable if practitioners do not control for the impact of experimental components (e.g., datasets, metrics, and classifiers) on defect prediction models, which could lead to erroneous decision-making in practice. In this thesis, we investigate the impact of experimental components on the performance and interpretation of defect prediction models. More specifically, we investigate the impact of the three often overlooked experimental components (i.e., issue report mislabelling, parameter optimization of classification techniques, and model validation techniques) have on defect prediction models. Through case studies of systems that span both proprietary and open-source domains, we demonstrate that (1) issue report mislabelling does not impact the precision of defect prediction models, suggesting that researchers can rely on the predictions of defect prediction models that were trained using noisy defect datasets; (2) automated parameter optimization for classification techniques substantially improve the performance and stability of defect prediction models, as well as they change their interpretation, suggesting that researchers should no longer shy from applying parameter optimization to their models; and (3) the out-of-sample bootstrap validation technique produces a good balance between bias and variance of performance estimates, suggesting that the single holdout and cross-validation families that are commonly-used nowadays should be avoided.Towards a Better Understanding of the Impact of Experimental Components on De...
Towards a Better Understanding of the Impact of Experimental Components on De...Chakkrit (Kla) Tantithamthavorn
ย
AbstractโCombinatorial testing (also called interaction testing) is an effective specification-based test input generation technique. By now most of research work in combinatorial testing aims to propose novel approaches trying to generate test suites with minimum size that still cover all the pairwise, triple, or n-way combinations of factors. Since the difficulty of solving this problem is demonstrated to be NP-hard, existing approaches have been designed to generate optimal or near optimal combinatorial test suites in polynomial time. In this paper, we try to apply particle swarm optimization (PSO), a kind of meta-heuristic search technique, to pairwise testing (i.e. a special case of combinatorial testing aiming to cover all the pairwise combinations). To systematically build pairwise test suites, we propose two different PSO based algorithms. One algorithm is based on one-test-at-a-time strategy and the other is based on IPO-like strategy. In these two different algorithms, we use PSO to complete the construction of a single test. To successfully apply PSO to cover more uncovered pairwise combinations in this construction process, we provide a detailed description on how to formulate the search space, define the fitness function and set some heuristic settings. To verify the effectiveness of our approach, we implement these algorithms and choose some typical inputs. In our empirical study, we analyze the impact factors of our approach and compare our approach to other well-known approaches. Final empirical results show the effectiveness and efficiency of our approach.A PARTICLE SWARM OPTIMIZATION TECHNIQUE FOR GENERATING PAIRWISE TEST CASES
A PARTICLE SWARM OPTIMIZATION TECHNIQUE FOR GENERATING PAIRWISE TEST CASESKula Sekhar Reddy Yerraguntla
ย
Defect models that are trained on class imbalanced datasets (i.e., the proportion of defective and clean modules is not equally represented) are highly susceptible to produce inaccurate prediction models. Prior research compares the impact of class rebalancing techniques on the performance of defect models but arrives at contradictory conclusions due to the use of different choice of datasets, classification techniques, and performance measures. Such contradictory conclusions make it hard to derive practical guidelines for whether class rebalancing techniques should be applied in the context of defect models. In this paper, we investigate the impact of class rebalancing techniques on performance measures and the interpretation of defect models. We also investigate the experimental settings in which class rebalancing techniques are beneficial for defect models. Through a case study of 101 datasets that span across proprietary and open-source systems, we conclude that the impact of class rebalancing techniques on the performance of defect prediction models depends on the used performance measure and the used classification techniques. We observe that the optimized SMOTE technique and the under-sampling technique are beneficial when quality assurance teams wish to increase AUC and Recall, respectively, but they should be avoided when deriving knowledge and understandings from defect models.The Impact of Class Rebalancing Techniques on the Performance and Interpretat...
The Impact of Class Rebalancing Techniques on the Performance and Interpretat...Chakkrit (Kla) Tantithamthavorn
ย
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Nature-Based Prediction Model of Bug Reports Based on Ensemble Machine Learning Model.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.comโ
Facebook: https://www.facebook.com/pages/Shakas-TechnologiesNature-Based Prediction Model of Bug Reports Based on Ensemble Machine Learni...
Nature-Based Prediction Model of Bug Reports Based on Ensemble Machine Learni...Shakas Technologies
ย
Software Quality Assurance (SQA) teams play a critical role in the software development process to ensure the absence of software defects. It is not feasible to perform exhaustive SQA tasks (i.e., software testing and code review) on a large software product given the limited SQA resources that are available. Thus, the prioritization of SQA efforts is an essential step in all SQA efforts. Defect prediction models are used to prioritize risky software modules and understand the impact of software metrics on the defect-proneness of software modules. The predictions and insights that are derived from defect prediction models can help software teams allocate their limited SQA resources to the modules that are most likely to be defective and avoid common past pitfalls that are associated with the defective modules of the past. However, the predictions and insights that are derived from defect prediction models may be inaccurate and unreliable if practitioners do not control for the impact of experimental components (e.g., datasets, metrics, and classifiers) on defect prediction models, which could lead to erroneous decision-making in practice. In this thesis, we investigate the impact of experimental components on the performance and interpretation of defect prediction models. More specifically, we investigate the impact of the three often overlooked experimental components (i.e., issue report mislabelling, parameter optimization of classification techniques, and model validation techniques) have on defect prediction models. Through case studies of systems that span both proprietary and open-source domains, we demonstrate that (1) issue report mislabelling does not impact the precision of defect prediction models, suggesting that researchers can rely on the predictions of defect prediction models that were trained using noisy defect datasets; (2) automated parameter optimization for classification techniques substantially improve the performance and stability of defect prediction models, as well as they change their interpretation, suggesting that researchers should no longer shy from applying parameter optimization to their models; and (3) the out-of-sample bootstrap validation technique produces a good balance between bias and variance of performance estimates, suggesting that the single holdout and cross-validation families that are commonly-used nowadays should be avoided.Towards a Better Understanding of the Impact of Experimental Components on De...
Towards a Better Understanding of the Impact of Experimental Components on De...Chakkrit (Kla) Tantithamthavorn
ย
AbstractโCombinatorial testing (also called interaction testing) is an effective specification-based test input generation technique. By now most of research work in combinatorial testing aims to propose novel approaches trying to generate test suites with minimum size that still cover all the pairwise, triple, or n-way combinations of factors. Since the difficulty of solving this problem is demonstrated to be NP-hard, existing approaches have been designed to generate optimal or near optimal combinatorial test suites in polynomial time. In this paper, we try to apply particle swarm optimization (PSO), a kind of meta-heuristic search technique, to pairwise testing (i.e. a special case of combinatorial testing aiming to cover all the pairwise combinations). To systematically build pairwise test suites, we propose two different PSO based algorithms. One algorithm is based on one-test-at-a-time strategy and the other is based on IPO-like strategy. In these two different algorithms, we use PSO to complete the construction of a single test. To successfully apply PSO to cover more uncovered pairwise combinations in this construction process, we provide a detailed description on how to formulate the search space, define the fitness function and set some heuristic settings. To verify the effectiveness of our approach, we implement these algorithms and choose some typical inputs. In our empirical study, we analyze the impact factors of our approach and compare our approach to other well-known approaches. Final empirical results show the effectiveness and efficiency of our approach.A PARTICLE SWARM OPTIMIZATION TECHNIQUE FOR GENERATING PAIRWISE TEST CASES
A PARTICLE SWARM OPTIMIZATION TECHNIQUE FOR GENERATING PAIRWISE TEST CASESKula Sekhar Reddy Yerraguntla
ย
Defect models that are trained on class imbalanced datasets (i.e., the proportion of defective and clean modules is not equally represented) are highly susceptible to produce inaccurate prediction models. Prior research compares the impact of class rebalancing techniques on the performance of defect models but arrives at contradictory conclusions due to the use of different choice of datasets, classification techniques, and performance measures. Such contradictory conclusions make it hard to derive practical guidelines for whether class rebalancing techniques should be applied in the context of defect models. In this paper, we investigate the impact of class rebalancing techniques on performance measures and the interpretation of defect models. We also investigate the experimental settings in which class rebalancing techniques are beneficial for defect models. Through a case study of 101 datasets that span across proprietary and open-source systems, we conclude that the impact of class rebalancing techniques on the performance of defect prediction models depends on the used performance measure and the used classification techniques. We observe that the optimized SMOTE technique and the under-sampling technique are beneficial when quality assurance teams wish to increase AUC and Recall, respectively, but they should be avoided when deriving knowledge and understandings from defect models.The Impact of Class Rebalancing Techniques on the Performance and Interpretat...
The Impact of Class Rebalancing Techniques on the Performance and Interpretat...Chakkrit (Kla) Tantithamthavorn
ย
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Elane - Promise08
- 1. Comparing Negative Binomial and Recursive Partitioning Models for Fault Prediction Elaine Weyuker Thomas Ostrand Robert Bell AT&T Labs - Research
- 2. High Level Goal To determine which files of a large industrial software system with multiple releases are particularly likely to be fault-prone.
- 5. Past Systems Studied 75% 2.25 years Voice Resp 83% 2 years Provisioning 83% 4 years Inventory 20% Files Period Covered System Type
- 7. Recursive Partitioning System A, Releases 1-26, cp = 0.01
- 8. ย
- 9. ย
- 10. ย
- 11. Percent Faults in 20% Files 67.9% 76.1% System C 84.8% 93.4% System B 76.1% 80.5% System A RP NBR