Analyzing developer contributions using artifact traceability graphs

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buir.advisorTüzün, Eray
dc.contributor.authorÇetin, Hamdi Alperen
dc.date.accessioned2021-02-03T12:33:10Z
dc.date.available2021-02-03T12:33:10Z
dc.date.copyright2020-12
dc.date.issued2020-12
dc.date.submitted2021-02-01
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2020.en_US
dc.descriptionIncludes bibliographical references (leaves 68-73).en_US
dc.description.abstractSoftware artifacts are the by-products of the development process. Throughout the life cycle of a project, developers produce different artifacts such as source files and bug reports. To analyze developer contributions, we construct artifact traceability graphs with these artifacts and their relations using the data from software development and collaboration tools. Developers are the main resource to build and maintain software projects. Since they keep the knowledge of the projects, developer turnover is a critical risk for software projects. From different viewpoints, some developers can be valuable and indispensable for the project. They are the key developers of the project, and identifying them is a crucial task for managerial decisions. Regardless of whether they are key developers or not, when developers leave the project, their work should be transferred to other developers. Even though all developers continue to work on the project, the knowledge distribution can be imbalanced among developers. Evaluating knowledge distribution is important since it might be an early warning for future problems. We employ algorithms on artifact traceability graphs to identify key develop-ers, recommend replacements for leaving developers and evaluate knowledge distribution among developers. We conduct experiments on six open source projects: Hadoop, Hive, Pig, HBase, Derby and Zookeeper. Then, we demonstrate that the identified key developers match the top commenters up to 98%, recommended replacements are correct up to 91% and identified knowledge distribution labels are compatible with the baseline approach up to 94%.en_US
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2021-02-03T12:33:10Z No. of bitstreams: 1 10374968.pdf: 881265 bytes, checksum: 2b228141870919ecdd4322bbfa4ceba8 (MD5)en
dc.description.provenanceMade available in DSpace on 2021-02-03T12:33:10Z (GMT). No. of bitstreams: 1 10374968.pdf: 881265 bytes, checksum: 2b228141870919ecdd4322bbfa4ceba8 (MD5) Previous issue date: 2021-02en
dc.description.statementofresponsibilityby Hamdi Alperen Çetinen_US
dc.embargo.release2021-08-01
dc.format.extentxii, 75 leaves : illustrarions, charts (some color) ; 30 cm.en_US
dc.identifier.itemidB151675
dc.identifier.urihttp://hdl.handle.net/11693/54975
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectKey developersen_US
dc.subjectSocial networksen_US
dc.subjectArtifact traceability graphsen_US
dc.subjectDeveloper replacementen_US
dc.subjectDeveloper turnoveren_US
dc.subjectKnowledge distributionen_US
dc.titleAnalyzing developer contributions using artifact traceability graphsen_US
dc.title.alternativeYapı izlenebilirlik çizgeleri kullanarak geliştirici katkılarını analiz etmeen_US
dc.typeThesisen_US
thesis.degree.disciplineComputer Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

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