TY - GEN
T1 - Can i solve it? identifying apis required to complete OSS tasks
AU - Santos, Fabio
AU - Wiese, Igor
AU - Trinkenreich, Bianca
AU - Steinmacher, Igor
AU - Sarma, Anita
AU - Gerosa, Marco A.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/5
Y1 - 2021/5
N2 - Open Source Software projects add labels to open issues to help contributors choose tasks. However, manually labeling issues is time-consuming and error-prone. Current automatic approaches for creating labels are mostly limited to classifying issues as a bug/non-bug. In this paper, we investigate the feasibility and relevance of labeling issues with the domain of the APIs required to complete the tasks. We leverage the issues' description and the project history to build prediction models, which resulted in precision up to 82% and recall up to 97.8%. We also ran a user study (n=74) to assess these labels' relevancy to potential contributors. The results show that the labels were useful to participants in choosing tasks, and the API-domain labels were selected more often than the existing architecture-based labels. Our results can inspire the creation of tools to automatically label issues, helping developers to find tasks that better match their skills.
AB - Open Source Software projects add labels to open issues to help contributors choose tasks. However, manually labeling issues is time-consuming and error-prone. Current automatic approaches for creating labels are mostly limited to classifying issues as a bug/non-bug. In this paper, we investigate the feasibility and relevance of labeling issues with the domain of the APIs required to complete the tasks. We leverage the issues' description and the project history to build prediction models, which resulted in precision up to 82% and recall up to 97.8%. We also ran a user study (n=74) to assess these labels' relevancy to potential contributors. The results show that the labels were useful to participants in choosing tasks, and the API-domain labels were selected more often than the existing architecture-based labels. Our results can inspire the creation of tools to automatically label issues, helping developers to find tasks that better match their skills.
KW - API identification
KW - Case Study
KW - Labelling
KW - Mining Software Repositories
KW - Multi-Label Classification
KW - Skills
KW - Tagging
UR - http://www.scopus.com/inward/record.url?scp=85113614854&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85113614854&partnerID=8YFLogxK
U2 - 10.1109/MSR52588.2021.00047
DO - 10.1109/MSR52588.2021.00047
M3 - Conference contribution
AN - SCOPUS:85113614854
T3 - Proceedings - 2021 IEEE/ACM 18th International Conference on Mining Software Repositories, MSR 2021
SP - 346
EP - 357
BT - Proceedings - 2021 IEEE/ACM 18th International Conference on Mining Software Repositories, MSR 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE/ACM International Conference on Mining Software Repositories, MSR 2021
Y2 - 17 May 2021 through 19 May 2021
ER -