RSTrace+: Reviewer suggestion using software artifact traceability graphs

buir.contributor.authorSülün, Emre
buir.contributor.authorTüzün, Eray
buir.contributor.authorDoğrusöz, Uğur
buir.contributor.orcidSülün, Emre|0000-0001-9513-1967
buir.contributor.orcidTüzün, Eray|0000-0002-5550-7816
buir.contributor.orcidDoğrusöz, Uğur|0000-0002-7153-0784
dc.citation.epage106455-13en_US
dc.citation.spage106455-1en_US
dc.citation.volumeNumber130en_US
dc.contributor.authorSülün, Emre
dc.contributor.authorTüzün, Eray
dc.contributor.authorDoğrusöz, Uğur
dc.date.accessioned2022-02-15T06:00:38Z
dc.date.available2022-02-15T06:00:38Z
dc.date.issued2021-02
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractContext: Various types of artifacts (requirements, source code, test cases, documents, etc.) are produced throughout the lifecycle of a software. These artifacts are connected 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 make sure the review is conducted properly, the reviewer(s) should be chosen appropriately. Objective: We previously introduced a novel approach, named RSTrace, to automatically recommend reviewers that are best suited based on their familiarity with a given artifact. In this study, we introduce an advanced version of RSTrace, named RSTrace+ that accounts for recency information of traceability links including practical tool support for GitHub. Methods: In this study, we conducted a series of experiments on finding the appropriate code reviewer(s) using RSTrace+ and provided a comparison with the other code reviewer recommendation approaches. Results: We had initially tested RSTrace+ on an open source project (Qt 3D Studio) and achieved a top-3 accuracy of 0.89 with an MRR (mean reciprocal ranking) of 0.81. In a further empirical evaluation of 40 open source projects, we compared RSTrace+ with Naive-Bayes, RevFinder and Profile based approach, and observed higher accuracies on the average. Conclusion: 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. Unlike other code reviewer recommendation approaches, RSTrace+ is not limited to recommending reviewers for source code artifacts and can potentially be used for recommending reviewers for other types of artifacts. Our approach can also visualize the affected artifacts and help the developer to make assessments of the potential impacts of change to the reviewed artifact.en_US
dc.embargo.release2023-02-28
dc.identifier.doi10.1016/j.infsof.2020.106455en_US
dc.identifier.issn0950-5849
dc.identifier.urihttp://hdl.handle.net/11693/77339
dc.language.isoEnglishen_US
dc.publisherElsevier BVen_US
dc.relation.isversionofhttps://doi.org/10.1016/j.infsof.2020.106455en_US
dc.source.titleInformation and Software Technologyen_US
dc.subjectSuggesting reviewersen_US
dc.subjectReviewer recommendationen_US
dc.subjectGraph miningen_US
dc.subjectSoftware traceabilityen_US
dc.subjectPull-request reviewen_US
dc.subjectModern code reviewen_US
dc.titleRSTrace+: Reviewer suggestion using software artifact traceability graphsen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
RSTrace+_Reviewer_suggestion_using_software_artifact_traceability_graphs.pdf
Size:
1.59 MB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.69 KB
Format:
Item-specific license agreed upon to submission
Description: