Software Developer Recommendation in Terms of Reducing Bug Tossing Length

Muhammad Zubair Baloch, Shahid Hussain, Humaira Afzal, Muhammad Rafiq Mufti, Bashir Ahmad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationSecurity, Privacy, and Anonymity in Computation, Communication, and Storage - 13th International Conference, SpaCCS 2020, Proceedings
EditorsGuojun Wang, Bing Chen, Wei Li, Roberto Di Pietro, Xuefeng Yan, Hao Han
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030688509
StatePublished - 2021
Event13th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2020 - Nanjing, China
Duration: Dec 18 2020Dec 20 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12382 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2020

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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