Partially observable Markov Decision Process to prioritize software defects

Background: Bug tracking systems receive many bug reports daily. Although the software quality team aims to identify and resolve these bugs, they are never able to fix all of the reported bugs in the issue tracking system before the release deadline. However, postponing the bug fixing may have some consequences. Prioritization of bug reports will help the software manager decide which bugs to fix and which bugs to postpone. Typically, bug reports are prioritized based on the severity, priority, time and effort for fixing, customer pressure, etc. Aim: Previous studies have shown that these factors may not be appropriate for prioritization. Therefore, relying on them to automate bug prioritization might be misleading. In this dissertation, we aim to prioritize bug reports with respect to the consequence of not fixing the bugs in terms of their relative importance in the issue tracking system. Method: In order to measure the relative importance of bugs in the issue tracking system, we propose the construction of a dependency graph based on the reported dependency-blocking information in the issue tracking system. Two metrics, namely depth and degree, are used to measure the relative importance of the bugs. However, there is uncertainty in the dependency graph structure as the dependency information is discovered manually and gradually. Owing to this uncertainty, prioritization of bugs in the descending order of depth and degree may be misleading. To handle the uncertainty, we propose a novel approach of a partially observable Markov decision process (POMDP) and partially observable Monte Carlo planning (POMCP). Result: To check the feasibility of the proposed approach, we analyzed seven years of data from an open source project, Firefox, and a commercial project. We compared the proposed policy with the developer policy, maximum policy, and random policy. Conclusion: The results suggest that software practitioners do not consider the relative importance of bugs in their current practice. The proposed framework can be combined with practitioners’ expertise to prioritize bugs more effectively and take the depth and degree of bugs into account. In practice, the POMDP framework with the POMCP planner can help practitioners sequentially select bugs to minimize the connectivity of the dependency graph.

Partially observable Markov Decision Process to prioritize software defects

Background: Bug tracking systems receive many bug reports daily. Although the software quality team aims to identify and resolve these bugs, they are never able to fix all of the reported bugs in the issue tracking system before the release deadline. However, postponing the bug fixing may have some consequences. Prioritization of bug reports will help the software manager decide which bugs to fix and which bugs to postpone. Typically, bug reports are prioritized based on the severity, priority, time and effort for fixing, customer pressure, etc. Aim: Previous studies have shown that these factors may not be appropriate for prioritization. Therefore, relying on them to automate bug prioritization might be misleading. In this dissertation, we aim to prioritize bug reports with respect to the consequence of not fixing the bugs in terms of their relative importance in the issue tracking system. Method: In order to measure the relative importance of bugs in the issue tracking system, we propose the construction of a dependency graph based on the reported dependency-blocking information in the issue tracking system. Two metrics, namely depth and degree, are used to measure the relative importance of the bugs. However, there is uncertainty in the dependency graph structure as the dependency information is discovered manually and gradually. Owing to this uncertainty, prioritization of bugs in the descending order of depth and degree may be misleading. To handle the uncertainty, we propose a novel approach of a partially observable Markov decision process (POMDP) and partially observable Monte Carlo planning (POMCP). Result: To check the feasibility of the proposed approach, we analyzed seven years of data from an open source project, Firefox, and a commercial project. We compared the proposed policy with the developer policy, maximum policy, and random policy. Conclusion: The results suggest that software practitioners do not consider the relative importance of bugs in their current practice. The proposed framework can be combined with practitioners’ expertise to prioritize bugs more effectively and take the depth and degree of bugs into account. In practice, the POMDP framework with the POMCP planner can help practitioners sequentially select bugs to minimize the connectivity of the dependency graph.

Are coupled file changes suggestions useful?

Background. Software maintenance is an important activity in the process of software engineering where over time maintenance team members leave and new members join. The identification of files being changes together frequently has been proposed several times. Yet, existing studies about these file changes ignore the feedback from developers as well as the impact on the performance of maintenance and rely on the analysis findings and expert evaluation. Methods. We conducted an experiment with the goal to investigate the usefulness of coupled file changes during maintenance tasks when developers are inexperienced in programming or when they are new on the project. Using data mining on software repositories we can identify files that changed most frequently together in the past. We extract coupled file changes from the Git repository of a Java software system and join them with corresponding attributes from the versioning and issue tracking system and the project documentation. We present a controlled experiment involving 36 student participants where we investigate if coupled file change suggestions influence the correctness of the task solutions and the time to complete them. Results. The results show that coupled file change suggestions significantly increase the correctness of the solutions. However, there is only a small effect on the time to complete the tasks. We also derived a set of the most useful attributes based on the developers feedback. Discussion. Coupled file changes and a limited number of the proposed attributes are useful for inexperienced developers working on maintenance tasks whereby although the developers using these suggestions solved more tasks, they still need time to organize and understand and implement this information.

Are coupled file changes suggestions useful?

Background. Software maintenance is an important activity in the process of software engineering where over time maintenance team members leave and new members join. The identification of files being changes together frequently has been proposed several times. Yet, existing studies about these file changes ignore the feedback from developers as well as the impact on the performance of maintenance and rely on the analysis findings and expert evaluation. Methods. We conducted an experiment with the goal to investigate the usefulness of coupled file changes during maintenance tasks when developers are inexperienced in programming or when they are new on the project. Using data mining on software repositories we can identify files that changed most frequently together in the past. We extract coupled file changes from the Git repository of a Java software system and join them with corresponding attributes from the versioning and issue tracking system and the project documentation. We present a controlled experiment involving 36 student participants where we investigate if coupled file change suggestions influence the correctness of the task solutions and the time to complete them. Results. The results show that coupled file change suggestions significantly increase the correctness of the solutions. However, there is only a small effect on the time to complete the tasks. We also derived a set of the most useful attributes based on the developers feedback. Discussion. Coupled file changes and a limited number of the proposed attributes are useful for inexperienced developers working on maintenance tasks whereby although the developers using these suggestions solved more tasks, they still need time to organize and understand and implement this information.