Assignment of bug reports to software developers using a multi-population evolutionary method

Simple item page
dc.contributor.authorAraujo, Kannya Leal
dc.contributor.authorMendes, Luiz Fernando
dc.contributor.authorAvelino, Guilherme
dc.contributor.authorRabelo, Ricardo
dc.contributor.authorOsaba, Eneko
dc.contributor.institutionQuantum
dc.date.accessioned2024-07-24T11:50:21Z
dc.date.available2024-07-24T11:50:21Z
dc.date.issued2022
dc.descriptionPublisher Copyright: © 2022 IEEE.
dc.description.abstractExisting approaches assign bug reports using only data from previously fixed reports. This can result in assignments to inactive developers, as well as not considering newcomers. A significant portion of assignments typically do not consider the workload of developers, which can overwhelm some and make the revision/debugging/correction process more time-consuming. This work proposes an approach for assigning bug reports that combines the experience and recent activities of developers, as well as their workload. When a new report is received, the effort to fix the bug based on similar error is estimated and each developer's affinity with the file containing the bug is calculated using a Fuzzy Inference system. Subsequently, the Golden Ball, a multi-population evolutionary metaheuristic, is used to assign these reports to developers according to affinity and workload. Experimental results show that, when compared with a brute force algorithm, the proposed approach reaches optimal values for assign in most cases (75% of the analyzed scenarios). The approach also obtained significantly better averages in 92.30% of the instances when compared to a Genetic Algorithm and 84.61% when compared to a Distributed Genetic Algorithm, and in only 23.07% of the instances there was no significant difference between the techniques.en
dc.description.statusPeer reviewed
dc.identifier.citationAraujo , K L , Mendes , L F , Avelino , G , Rabelo , R & Osaba , E 2022 , Assignment of bug reports to software developers using a multi-population evolutionary method . in Proceedings - 2022 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2022 . Proceedings - 2022 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2022 , Institute of Electrical and Electronics Engineers Inc. , 8th IEEE Latin American Conference on Computational Intelligence, LA-CCI 2022 , Montevideo , Uruguay , 22/11/22 . https://doi.org/10.1109/LA-CCI54402.2022.9981348
dc.identifier.citationconference
dc.identifier.doi10.1109/LA-CCI54402.2022.9981348
dc.identifier.isbn9781665488587
dc.identifier.urihttps://hdl.handle.net/11556/1968
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85146255633&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofProceedings - 2022 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2022
dc.relation.ispartofseriesProceedings - 2022 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2022
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsBug file affinity
dc.subject.keywordsBug report
dc.subject.keywordsBug triage
dc.subject.keywordsDeveloper workload
dc.subject.keywordsFuzzy system
dc.subject.keywordsGolden ball
dc.subject.keywordsMulti-population evolutionary method
dc.subject.keywordsStack trace
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsComputer Science Applications
dc.titleAssignment of bug reports to software developers using a multi-population evolutionary methoden
dc.typeconference output
Files