research-article

Reviewer Recommendation using Software Artifact Traceability Graphs

Authors:

Uğur DoğrusözAuthors Info & Claims

PROMISE'19: Proceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering

Pages 66 - 75

https://doi.org/10.1145/3345629.3345637

Published: 18 September 2019 Publication History

Abstract

Various types of artifacts (requirements, source code, test cases, documents, etc.) are produced throughout the lifecycle of a software. These artifacts are often related with each other via traceability links that are stored in modern application lifecycle management repositories. Throughout the lifecycle of a software, various types of changes can arise in any one of these artifacts. It is important to review such changes to minimize their potential negative impacts. To maximize benefits of the review process, the reviewer(s) should be chosen appropriately.

In this study, we reformulate the reviewer suggestion problem using software artifact traceability graphs. We introduce a novel approach, named RSTrace, to automatically recommend reviewers that are best suited based on their familiarity with a given artifact. The proposed approach, in theory, could be applied to all types of artifacts. For the purpose of this study, we focused on the source code artifact and conducted an experiment on finding the appropriate code reviewer(s). We initially tested RSTrace on an open source project and achieved top-3 recall of 0.85 with an MRR (mean reciprocal ranking) of 0.73. In a further empirical evaluation of 37 open source projects, we confirmed that the proposed reviewer recommendation approach yields promising top-k and MRR scores on the average compared to the existing reviewer recommendation approaches.

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Cited By

Cury OAvelino GNeto PValente MBritto R(2024)Source code expert identificationInformation and Software Technology10.1016/j.infsof.2024.107445170:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.infsof.2024.107445
Badampudi DUnterkalmsteiner MBritto R(2023)Modern Code Reviews—Survey of Literature and PracticeACM Transactions on Software Engineering and Methodology10.1145/358500432:4(1-61)Online publication date: 26-May-2023
https://dl.acm.org/doi/10.1145/3585004
Zhou XKim KXu BHan DHe JLo D(2023)Generation-based Code Review Automation: How Far Are We?2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC)10.1109/ICPC58990.2023.00036(215-226)Online publication date: May-2023
https://doi.org/10.1109/ICPC58990.2023.00036
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Index Terms

Reviewer Recommendation using Software Artifact Traceability Graphs
1. Software and its engineering
  1. Software creation and management
    1. Collaboration in software development
  2. Software notations and tools
    1. Software maintenance tools

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PROMISE'19: Proceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering

September 2019

103 pages

ISBN:9781450372336

DOI:10.1145/3345629

General Chair:
Leandro Minku,
Program Chairs:
Foutse Khomh,
Jean Petrić

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 18 September 2019

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PROMISE'19

PROMISE'19: The Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering

September 18, 2019

Recife, Brazil

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Overall Acceptance Rate 98 of 213 submissions, 46%

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Cited By

Cury OAvelino GNeto PValente MBritto R(2024)Source code expert identificationInformation and Software Technology10.1016/j.infsof.2024.107445170:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.infsof.2024.107445
Badampudi DUnterkalmsteiner MBritto R(2023)Modern Code Reviews—Survey of Literature and PracticeACM Transactions on Software Engineering and Methodology10.1145/358500432:4(1-61)Online publication date: 26-May-2023
https://dl.acm.org/doi/10.1145/3585004
Zhou XKim KXu BHan DHe JLo D(2023)Generation-based Code Review Automation: How Far Are We?2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC)10.1109/ICPC58990.2023.00036(215-226)Online publication date: May-2023
https://doi.org/10.1109/ICPC58990.2023.00036
De Luca MFasolino AFerraro AMoscato VSperlí GTramontana P(2023)A community detection approach based on network representation learning for repository miningExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120597231:COnline publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120597
Ahasanuzzaman MOliva GHassan A(2023)Using knowledge units of programming languages to recommend reviewers for pull requests: an empirical studyEmpirical Software Engineering10.1007/s10664-023-10421-929:1Online publication date: 29-Dec-2023
https://dl.acm.org/doi/10.1007/s10664-023-10421-9
Cury OAvelino GSantos Neto PBritto RTúlio Valente M(2022)Identifying Source Code File ExpertsProceedings of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3544902.3546243(125-136)Online publication date: 19-Sep-2022
https://dl.acm.org/doi/10.1145/3544902.3546243
Jiang JLv JZheng JZhang L(2022)How Developers Modify Pull Requests in Code ReviewIEEE Transactions on Reliability10.1109/TR.2021.309315971:3(1325-1339)Online publication date: Sep-2022
https://doi.org/10.1109/TR.2021.3093159
Fucci DAlégroth EAxelsson T(2022)When traceability goes awryJournal of Systems and Software10.1016/j.jss.2022.111389192:COnline publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.jss.2022.111389
Tecimer KTüzün EMoran CErdogmus H(2022)Cleaning ground truth data in software task assignmentInformation and Software Technology10.1016/j.infsof.2022.106956149:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.infsof.2022.106956
Çetin HTüzün E(2022)Analyzing developer contributions using artifact traceability graphsEmpirical Software Engineering10.1007/s10664-022-10129-227:3Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s10664-022-10129-2
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