Amalgamating source code authors, maintainers, and change proneness to triage change requests

Hossen, Kamal; Kagdi, Huzefa; Poshyvanyk, Denys

doi:10.1145/2597008.2597147

Cited by 29 publications

(22 citation statements)

References 34 publications

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“…A straightforward measure of the ICP of a source code entity is given by the number of commits in commit history that contain that source code entity [46]. The rationale behind this choice for re-ranking the set of the recommendation is based on the premise that the larger the number of changes related to past requests (e.g., bug fixes) in which the relevant source code file is involved, the higher the likelihood of the same file requiring changes due to a given (new) change request.…”

Section: Re-ranking the Recommended Source Code Files With Issue Chanmentioning

confidence: 99%

“…The first approach, xFinder [53], is based on the mining of commit logs. The second approach, iMacPro [46], uses the authorship information and maintainers of the relevant change prone source code to recommend developers. We used these two approaches for comparison because they require information from the commit repository.…”

Section: Case Studymentioning

confidence: 99%

“…Finally, a final ranked list of developers for the given change request is recommended. Hossen et al [46] showed that iMacPro outperformed the iA approach [65], which was shown to perform equivalent to or better than the approach of Anvik et al [9].…”

Section: Compared Approaches: Xfinder Xfinder and Imacpromentioning

confidence: 99%

“…The fundamental idea underlying most triage approaches is to identify the expertise and interests of developers, infer the concern or component that must be addressed for a task, and then match developers to tasks. Approaches typically operate on the information available from software repositories (e.g., models trained from past bugs/issues or source code changes), the source code authorship [65], or their combinations [46] under the rationale that if a developer frequently commits changes to, or fixes bugs in, particular parts of a system, they have knowledge of that area and can competently make changes to it.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, our iHDev approach is empirically compared with two other approaches that use the commit and/or source code authorship information [65], [46], [53]. The results show that the presented iHDev approach outperformed these baseline competitors.…”

Section: Introductionmentioning

confidence: 99%

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Effective assignment and assistance to software developers and reviewers

Zanjani

2016

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

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DEDICATIONTo my parents and my sister. In this dissertation, we first present the automated solutions for Software Change Impact Analysis based on interaction and code review activities of developers. Then we provide explanation for two separate developer expertise models which use the micro-levels of human-to-code and human-to-human interactions from the previous code review and interaction activities of developers. Next, we present a reviewer expertise model based on code review activities of developers and show how this expertise model can be used for Code Reviewer Recommendation. At the end we examine the influential features that characterize the acceptance probability of a submitted patch (implemented code change) by developers. We present a predictive model that classifies whether a patch will be accepted or not as soon as it is submitted for code review in order to assist developers and reviewers in prioritizing and focusing their efforts.A rigorous empirical validation on large open source and commercial systems shows that the solutions based on the presented methodology outperform several existing solutions. The quantitative gains of our solutions across a spectrum of evaluation metrics along with their statistical significance are reported.vi

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Section: Re-ranking the Recommended Source Code Files With Issue Chanmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Compared Approaches: Xfinder Xfinder and Imacpromentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effective assignment and assistance to software developers and reviewers

Zanjani

2016

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

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Dual analysis for recommending developers to resolve bugs

Xia

Wang

et al. 2015

J Software Evolu Process

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Bug resolution refers to the activity that developers perform to diagnose, fix, test, and document bugs during software development and maintenance. Given a bug report, we would like to recommend the set of bug resolvers that could potentially contribute their knowledge to fix it. We refer to this problem as developer recommendation for bug resolution. In this paper, we propose a new and accurate method named DevRec for the developer recommendation problem. DevRec is a composite method that performs two kinds of analysis: bug reports based analysis (BR-Based analysis) and developer based analysis (D-Based analysis). We evaluate our solution on five large bug report datasets including GNU Compiler Collection, OpenOffice, Mozilla, Netbeans, and Eclipse containing a total of 107,875 bug reports. We show that DevRec could achieve recall@5 and recall@10 scores of 0.4826-0.7989, and 0.6063-0.8924, respectively. The results show that DevRec on average improves recall@5 and recall@10 scores of Bugzie by 57.55% and 39.39%, outperforms DREX by 165.38% and 89.36%, and outperforms NonTraining by 212.39% and 168.01%, respectively. Moreover, we evaluate the stableness of DevRec with different parameters, and the results show that the performance of DevRec is stable for a wide range of parameters.

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An empirical investigation of single‐objective and multiobjective evolutionary algorithms for developer's assignment to bugs

Karim

Ruhe

Rahman³

et al. 2016

J Software Evolu Process

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In this paper, the modeling of developers' assignment to bugs (DAB) is studied. The problem is modeled both as a single objective (minimize bug fix time) and as a bi-objective (minimize bug fix time and cost) combinatorial optimization problem. Two models of developer assignment are considered where in the first model a single developer is assigned per bug (single developer model), while in the second model a single developer is assigned for each competency area of a bug (individual competency model). The latter model is proposed in this paper. For the single developer model, GA@DAB, an existing genetic algorithm-based approach, is extended to support precedence among bugs. For the individual competency model of DAB, one genetic algorithm-based approach (Competence@DAB) and one nondominated sorting genetic algorithm II-based approach (CompetenceMulti 2 @DAB ) are proposed to generate solutions minimizing time and minimizing both time and cost, respectively. The performance of the proposed approaches was evaluated for 2040 bugs of 19 open-source milestone projects from the ECLIPSE platform. Our results and analysis show that the proposed individual competency model is far better than the single developer model, with average bug fix time reduction of 39.7% across all projects.

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Amalgamating source code authors, maintainers, and change proneness to triage change requests

Cited by 29 publications

References 34 publications

Effective assignment and assistance to software developers and reviewers

Effective assignment and assistance to software developers and reviewers

Dual analysis for recommending developers to resolve bugs

An empirical investigation of single‐objective and multiobjective evolutionary algorithms for developer's assignment to bugs

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