Suggesting reviewers of software artifacts using traceability graphs
buir.contributor.author | Sülün, Emre | |
dc.citation.epage | 1252 | en_US |
dc.citation.spage | 1250 | en_US |
dc.contributor.author | Sülün, Emre | en_US |
dc.contributor.editor | Apel, S. | |
dc.contributor.editor | Dumas, M. | |
dc.contributor.editor | Russo, A. | |
dc.contributor.editor | Pfahl, D. | |
dc.coverage.spatial | Tallin, Estonia | en_US |
dc.date.accessioned | 2020-01-30T08:00:39Z | |
dc.date.available | 2020-01-30T08:00:39Z | |
dc.date.issued | 2019 | |
dc.department | Department of Computer Engineering | en_US |
dc.description | Date of Conference: 26-30 August 2019 | en_US |
dc.description | Conference Name: 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2019 | en_US |
dc.description.abstract | During the lifecycle of a software project, software artifacts constantly change. A change should be peer-reviewed to ensure the software quality. To maximize the benefit of review, the reviewer(s) should be chosen appropriately. However, choosing the right reviewer(s) might not be trivial especially in large projects. Researchers developed different methods to recommend reviewers. In this study, we introduce a novel approach for reviewer recommendation problem. Our approach utilizes the traceability graph of a software project and assigns a know-about score to each developer, then recommends the developers who have the maximum know-about score for an artifact. We tested our approach on an open source project and achieved top-3 recall of 0.85 with an MRR (mean reciprocal ranking) of 0.73. | en_US |
dc.description.provenance | Submitted by Zeynep Aykut (zeynepay@bilkent.edu.tr) on 2020-01-30T08:00:39Z No. of bitstreams: 1 Suggesting_reviewers_of_software_artifacts_using_traceability_graphs.pdf: 591910 bytes, checksum: 07ed9c5b649c70ce500c6e8c713c26c8 (MD5) | en |
dc.description.provenance | Made available in DSpace on 2020-01-30T08:00:39Z (GMT). No. of bitstreams: 1 Suggesting_reviewers_of_software_artifacts_using_traceability_graphs.pdf: 591910 bytes, checksum: 07ed9c5b649c70ce500c6e8c713c26c8 (MD5) Previous issue date: 2019 | en |
dc.description.sponsorship | ACM SIGSOFT | en_US |
dc.identifier.doi | 10.1145/3338906.3342507 | en_US |
dc.identifier.isbn | 9781450355728 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/52920 | en_US |
dc.language.iso | English | en_US |
dc.publisher | Association for Computing Machinery, Inc | en_US |
dc.relation.isversionof | https://dx.doi.org/10.1145/3338906.3342507 | en_US |
dc.source.title | Proceedings of the 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2019 | en_US |
dc.subject | Reviewer recommendation | en_US |
dc.subject | Code review | en_US |
dc.subject | Software traceability | en_US |
dc.title | Suggesting reviewers of software artifacts using traceability graphs | en_US |
dc.type | Conference Paper | en_US |
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