WhoseFault: Automatic developer-to-fault assignment through fault localization

Servant, Francisco; Jones, Jones

doi:10.1109/icse.2012.6227208

Cited by 30 publications

(9 citation statements)

References 29 publications

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“…Servant and Jones (2012) developed WhoseFault, a tool that both assigns a bug to a developer and presents a possible location of the fault in the source code. WhoseFault is also dierent from other approaches reported in this section, as it performs its analysis originating from failures from automated testing instead of textual bug reports.…”

Section: Other Approaches To Automated Bug Assignmentmentioning

confidence: 99%

Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

et al. 2015

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Context: Bug report assignment is an important part of software maintenance. In particular, incorrect assignments of bug reports to development teams can be very expensive in large software development projects.Several studies propose automating bug assignment techniques using machine learning in open source software contexts, but no study exists for large-scale proprietary projects in industry. Objective: The goal of this study is to evaluate automated bug assignment techniques that are based on machine learning classication. In particular, we study the state-of-the-art ensemble learner Stacked Generalization (SG) that combines several classiers. Method: We collect more than 50,000 bug reports from ve development projects from two companies in dierent domains. We implement automated bug assignment and evaluate the performance in a set of controlled experiments. Results: We show that SG scales to large scale industrial application and that it outperforms the use of individual classiers for bug assignment, reaching prediction accuracies from 50% to 90% when large training sets are used. In addition, we show how old training data can decrease the prediction accuracy of bug assignment. Conclusions: We advice industry to use SG for bug assignment in proprietary contexts, using at least 2,000 bug reports for training. Finally, we highlight the importance of not solely relying on results from cross-validation when evaluating automated bug assignment.

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Section: Other Approaches To Automated Bug Assignmentmentioning

confidence: 99%

Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

et al. 2015

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“…By employing multiple techniques such as machine-learning algorithms and social network analysis, the previous studies [29,30,[36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53] can achieve the purpose of automatic assignee recommendation. According to the different techniques, we classify these studies into five category such as machine learning-based recommender, expertise model-based recommender, tossing graph-based recommender, social network-based recommender and topic model-based recommender, which are detailed in the following subsections, respectively.…”

Section: Bug Triagementioning

confidence: 99%

“…Except for reassignments, processing a multitude of bug reports places a heavy burden on triagers. To resolve this problem, several automatic bug triage approaches [29,30,[36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53] have been proposed to recommend the best developers for fixing the given bugs. (3) Bug fixing: The assigned developers, usually called as assignees, are responsible for fixing the reported bugs.…”

Section: Introductionmentioning

confidence: 99%

A Literature Review of Research in Bug Resolution: Tasks, Challenges and Future Directions

Zhang

Jiang

Wang

et al. 2015

The Computer Journal

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Due to the increasing scale and complexity of software products, software maintenance especially on bug resolution has become a challenging task. Generally in large-scale software programs, developers depend on software artifacts (e.g., bug report, source code and change history) in bug repositories to complete the bug resolution task. However, a mountain of submitted bug reports every day increase the developers' workload. Therefore, 'How to effectively resolve software defects by utilizing software artifacts?' becomes a research hotspot in software maintenance. Considerable studies have been done on bug resolution by using multi-techniques, which cover data mining, machine learning and natural language processing. In this paper, we present a literature survey on tasks, challenges and future directions of bug resolution in software maintenance process. Our investigation concerns the most important phases in bug resolution, including bug understanding, bug triage and bug fixing. Moreover, we present the advantages and disadvantages of each study. Finally, based on the investigation and comparison results, we propose the future research directions of bug resolution.

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“…In a study [26] authors outlined an approach based on information retrieval in which they report recall levels of around 20% for Mozilla. A new technique which automatically selects the most appropriate developers for fixing the fault represented by a failing test case has been proposed by [27]. Their technique is the first to assign developers to execution failures without the need for textual bug reports.…”

Section: Related Workmentioning

confidence: 99%

Bug Assignee Prediction Using Association Rule Mining

Sharma

Kumari

Singh

2015

Computational Science and Its Applications -- ICCSA 2015

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Abstract. In open source software development we have bug repository to which both developers and users can report bugs. Bug triage, deciding what to do with an incoming bug report, takes a large amount of developer resources and time. All newly coming bug reports must be triaged to determine whether the report is correct and requires attention and if it is, which potentially experienced developer/fixer will be assigned the responsibility of resolving the bug report. In this paper, we propose to apply association mining to assist in bug triage by using Apriori algorithm to predict the developer that should work on the bug based on the bug's severity, priority and summary terms. We demonstrate our approach on collection of 1,695 bug reports of Thunderbird, AddOnSDK and Bugzilla products of Mozilla open source project. We have analyzed the association rules for top five assignee of the three products. Association rules can support the managers to improve its process during development and save time and resources.

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WhoseFault: Automatic developer-to-fault assignment through fault localization

Cited by 30 publications

References 29 publications

Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

A Literature Review of Research in Bug Resolution: Tasks, Challenges and Future Directions

Bug Assignee Prediction Using Association Rule Mining

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