Show simple item record

dc.contributor.authorSülün, Emreen_US
dc.contributor.authorTüzün, Erayen_US
dc.contributor.authorDoğrusöz, Uğuren_US
dc.coverage.spatialRecife, Brazilen_US
dc.date.accessioned2020-01-30T08:29:16Z
dc.date.available2020-01-30T08:29:16Z
dc.date.issued2019
dc.identifier.isbn9781450372336
dc.identifier.urihttp://hdl.handle.net/11693/52922
dc.descriptionDate of Conference: 18 September 2019en_US
dc.descriptionConference Name: 15th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2019en_US
dc.description.abstractVarious 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.en_US
dc.language.isoEnglishen_US
dc.source.titleProceedings of the 15th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2019en_US
dc.relation.isversionofhttps://dx.doi.org/10.1145/3345629.3345637en_US
dc.subjectSuggesting reviewersen_US
dc.subjectReviewer recommendationen_US
dc.subjectCode reviewen_US
dc.subjectSoftware traceabilityen_US
dc.subjectPull-request reviewen_US
dc.subjectModern code reviewen_US
dc.titleReviewer recommendation using software artifact traceability graphsen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage66en_US
dc.citation.epage75en_US
dc.identifier.doi10.1145/3345629.3345637en_US
dc.publisherAssociation for Computing Machineryen_US
dc.contributor.bilkentauthorSülün, Emre
dc.contributor.bilkentauthorTüzün, Eray
dc.contributor.bilkentauthorDoğrusöz, Uğur


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record