Software defect prediction: do different classifiers find the same defects?

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio

Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)

We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results. Hence, this paper applies (a) multi-criteria tests while (b) fixing the weaker regions of the training data (using SMOTUNED, which is a self-tuning version of SMOTE). This approach leads to dramatically large increases in software defect predictions. When applied in a 5*5 cross-validation study for 3,681 JAVA classes (containing over a million lines of code) from open source systems, SMOTUNED increased AUC and recall by 60% and 20% respectively. These improvements are independent of the classifier used to predict for quality. Same kind of pattern (improvement) was observed when a comparative analysis of SMOTE and SMOTUNED was done against the most recent class imbalance technique. In conclusion, for software analytic tasks like defect prediction, (1) data pre-processing can be more important than classifier choice, (2) ranking studies are incomplete without such pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.Comment: 10 pages + 2 references. Accepted to International Conference of Software Engineering (ICSE), 201

Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)

We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results. Hence, this paper applies (a) multi-criteria tests while (b) fixing the weaker regions of the training data (using SMOTUNED, which is a self-tuning version of SMOTE). This approach leads to dramatically large increases in software defect predictions. When applied in a 5*5 cross-validation study for 3,681 JAVA classes (containing over a million lines of code) from open source systems, SMOTUNED increased AUC and recall by 60% and 20% respectively. These improvements are independent of the classifier used to predict for quality. Same kind of pattern (improvement) was observed when a comparative analysis of SMOTE and SMOTUNED was done against the most recent class imbalance technique. In conclusion, for software analytic tasks like defect prediction, (1) data pre-processing can be more important than classifier choice, (2) ranking studies are incomplete without such pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.Comment: 10 pages + 2 references. Accepted to International Conference of Software Engineering (ICSE), 201

Is One Hyperparameter Optimizer Enough?

Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. While widely applied in empirical Software Engineering, there has not been much discussion on which hyperparameter tuner is best for software analytics. To address this gap in the literature, this paper applied a range of hyperparameter optimizers (grid search, random search, differential evolution, and Bayesian optimization) to defect prediction problem. Surprisingly, no hyperparameter optimizer was observed to be `best' and, for one of the two evaluation measures studied here (F-measure), hyperparameter optimization, in 50\% cases, was no better than using default configurations. We conclude that hyperparameter optimization is more nuanced than previously believed. While such optimization can certainly lead to large improvements in the performance of classifiers used in software analytics, it remains to be seen which specific optimizers should be applied to a new dataset.Comment: 7 pages, 2 columns, accepted for SWAN1

Quality-Aware Learning to Prioritize Test Cases

Software applications evolve at a rapid rate because of continuous functionality extensions, changes in requirements, optimization of code, and fixes of faults. Moreover, modern software is often composed of components engineered with different programming languages by different internal or external teams. During this evolution, it is crucial to continuously detect unintentionally injected faults and continuously release new features. Software testing aims at reducing this risk by running a certain suite of test cases regularly or at each change of the source code. However, the large number of test cases makes it infeasible to run all test cases. Automated test case prioritization and selection techniques have been studied in order to reduce the cost and improve the efficiency of testing tasks. However, the current state-of-art techniques remain limited in some aspects. First, the existing test prioritization and selection techniques often assume that faults are equally distributed across the software components, which can lead to spending most of the testing budget on components less likely to fail rather than the ones highly to contain faults. Second, the existing techniques share a scalability problem not only in terms of the size of the selected test suite but also in terms of the round-trip time between code commits and engineer feedback on test cases failures in the context of Continuous Integration (CI) development environments. Finally, it is hard to algorithmically capture the domain knowledge of the human testers which is crucial in testing and release cycles. This thesis is a new take on the old problem of reducing the cost of software testing in these regards by presenting a data-driven lightweight approach for test case prioritization and execution scheduling that is being used (i) during CI cycles for quick and resource-optimal feedback to engineers, and (ii) during release planning by capturing the testers domain knowledge and release requirements. Our approach combines software quality metrics with code churn metrics to build a regressive model that predicts the fault density of each component and a classification model to discriminate faulty from non-faulty components. Both models are used to guide the testing effort to the components likely to contain the largest number of faults. The predictive models have been validated on eight industrial automotive software applications at Daimler, showing a classification accuracy of 89% and an accuracy of 85.7% for the regression model. The thesis develops a test cases prioritization model based on features of the code change, the tests execution history and the component development history. The model reduces the cost of CI by predicting whether a particular code change should trigger the individual test suites and their corresponding test cases. In order to algorithmically capture the domain knowledge and the preferences of the tester, our approach developed a test case execution scheduling model that consumes the testers preferences in the form of a probabilistic graph and solves the optimal test budget allocation problem both online in the context of CI cycles and offline when planning a release. Finally, the thesis presents a theoretical cost model that describes when our prioritization and scheduling approach is worthwhile. The overall approach is validated on two industrial analytical applications in the area of energy management and predictive maintenance, showing that over 95% of the test failures are still reported back to the engineers while only 43% of the total available test cases are being executed

Quality-Aware Learning to Prioritize Test Cases

Software applications evolve at a rapid rate because of continuous functionality extensions, changes in requirements, optimization of code, and fixes of faults. Moreover, modern software is often composed of components engineered with different programming languages by different internal or external teams. During this evolution, it is crucial to continuously detect unintentionally injected faults and continuously release new features. Software testing aims at reducing this risk by running a certain suite of test cases regularly or at each change of the source code. However, the large number of test cases makes it infeasible to run all test cases. Automated test case prioritization and selection techniques have been studied in order to reduce the cost and improve the efficiency of testing tasks. However, the current state-of-art techniques remain limited in some aspects. First, the existing test prioritization and selection techniques often assume that faults are equally distributed across the software components, which can lead to spending most of the testing budget on components less likely to fail rather than the ones highly to contain faults. Second, the existing techniques share a scalability problem not only in terms of the size of the selected test suite but also in terms of the round-trip time between code commits and engineer feedback on test cases failures in the context of Continuous Integration (CI) development environments. Finally, it is hard to algorithmically capture the domain knowledge of the human testers which is crucial in testing and release cycles. This thesis is a new take on the old problem of reducing the cost of software testing in these regards by presenting a data-driven lightweight approach for test case prioritization and execution scheduling that is being used (i) during CI cycles for quick and resource-optimal feedback to engineers, and (ii) during release planning by capturing the testers domain knowledge and release requirements. Our approach combines software quality metrics with code churn metrics to build a regressive model that predicts the fault density of each component and a classification model to discriminate faulty from non-faulty components. Both models are used to guide the testing effort to the components likely to contain the largest number of faults. The predictive models have been validated on eight industrial automotive software applications at Daimler, showing a classification accuracy of 89% and an accuracy of 85.7% for the regression model. The thesis develops a test cases prioritization model based on features of the code change, the tests execution history and the component development history. The model reduces the cost of CI by predicting whether a particular code change should trigger the individual test suites and their corresponding test cases. In order to algorithmically capture the domain knowledge and the preferences of the tester, our approach developed a test case execution scheduling model that consumes the testers preferences in the form of a probabilistic graph and solves the optimal test budget allocation problem both online in the context of CI cycles and offline when planning a release. Finally, the thesis presents a theoretical cost model that describes when our prioritization and scheduling approach is worthwhile. The overall approach is validated on two industrial analytical applications in the area of energy management and predictive maintenance, showing that over 95% of the test failures are still reported back to the engineers while only 43% of the total available test cases are being executed