TY - GEN
T1 - Software Developer Recommendation in Terms of Reducing Bug Tossing Length
AU - Baloch, Muhammad Zubair
AU - Hussain, Shahid
AU - Afzal, Humaira
AU - Mufti, Muhammad Rafiq
AU - Ahmad, Bashir
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - In close software development, it is easy for the project manager to recommend the right developer to resolve a bug that is reported by an end-user. However, in the case of open-source software developments, where most developers are engaged on different project either on the same or different repositories. Due to their agile involvement on repositories, bug triaging might be slow and increases the Bug Tossing Length (BTL) which is encounter as the time between reporting and resolving bugs. In open-source software repositories like GitHub, numerous developers are involved with well-known projects to resolve the issue reported by end-users. The assignment of the reported bug to an appropriate developer may lead to a reduced BTL time. Though, several metrics based and Machine Learning (ML) based approaches have been introduced to recommend the appropriate developer on the bases of several parameters. However, few studies are related to the recommendation of developers on the bases of their historical information regarding their attempts to reduce the BTL. To address this issue, we have proposed a new approach to recommend a developer for bug triaging on the bases of their involvement in reducing the BTL. In the proposed study, the model is trained once and new bug reports are automatically assigned to relevant developers. In this regard, we exploit the proposed methodology through using the XGBoost, Support Vector Machine, Random Forest, Decision Tree, KNearest Neighbor, and Naïve Bayes for the recommendation of the developer for a reported bug. We used widely-known two datasets namely Eclipse, and Mozilla. The experimental result indicate the effectiveness of proposed methodology in terms of developer recommendation for a new reported bug.
AB - In close software development, it is easy for the project manager to recommend the right developer to resolve a bug that is reported by an end-user. However, in the case of open-source software developments, where most developers are engaged on different project either on the same or different repositories. Due to their agile involvement on repositories, bug triaging might be slow and increases the Bug Tossing Length (BTL) which is encounter as the time between reporting and resolving bugs. In open-source software repositories like GitHub, numerous developers are involved with well-known projects to resolve the issue reported by end-users. The assignment of the reported bug to an appropriate developer may lead to a reduced BTL time. Though, several metrics based and Machine Learning (ML) based approaches have been introduced to recommend the appropriate developer on the bases of several parameters. However, few studies are related to the recommendation of developers on the bases of their historical information regarding their attempts to reduce the BTL. To address this issue, we have proposed a new approach to recommend a developer for bug triaging on the bases of their involvement in reducing the BTL. In the proposed study, the model is trained once and new bug reports are automatically assigned to relevant developers. In this regard, we exploit the proposed methodology through using the XGBoost, Support Vector Machine, Random Forest, Decision Tree, KNearest Neighbor, and Naïve Bayes for the recommendation of the developer for a reported bug. We used widely-known two datasets namely Eclipse, and Mozilla. The experimental result indicate the effectiveness of proposed methodology in terms of developer recommendation for a new reported bug.
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U2 - 10.1007/978-3-030-68851-6_29
DO - 10.1007/978-3-030-68851-6_29
M3 - Conference contribution
AN - SCOPUS:85101832822
SN - 9783030688509
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 396
EP - 407
BT - Security, Privacy, and Anonymity in Computation, Communication, and Storage - 13th International Conference, SpaCCS 2020, Proceedings
A2 - Wang, Guojun
A2 - Chen, Bing
A2 - Li, Wei
A2 - Di Pietro, Roberto
A2 - Yan, Xuefeng
A2 - Han, Hao
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2020
Y2 - 18 December 2020 through 20 December 2020
ER -