Araujo, Kannya LealMendes, Luiz FernandoAvelino, GuilhermeRabelo, RicardoOsaba, Eneko2024-07-242024-07-242022Araujo , 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-CCI54402.2022.9981348conference9781665488587https://hdl.handle.net/11556/1968Publisher Copyright: © 2022 IEEE.Existing 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.enginfo:eu-repo/semantics/restrictedAccessconference output10.1109/LA-CCI54402.2022.9981348Bug file affinityBug reportBug triageDeveloper workloadFuzzy systemGolden ballMulti-population evolutionary methodStack traceArtificial IntelligenceComputer Science Applicationshttp://www.scopus.com/inward/record.url?scp=85146255633&partnerID=8YFLogxK