Classification and intuitionistic fuzzy set based software bug triaging techniques

Software development is a modular approach involving multiple developers and multi-tasking teams working at different locations. A particular term in a software bug can belong to multiple modules and multiple developers' profiles. Also, many people who report software bugs are unfamiliar with the exact technical terminology of software development, which causes the software bug to be unlabeled, vague, and noisy. Hence, analyzing, understanding, and assigning the newly reported bugs to the most appropri-ate developer is a challenging task for the triager. Intuitionistic Fuzzy Sets (IFS) consider the non-membership and hesitant values along with the membership values of the software bug terms mapped to the developers and thus provide a powerful tool for better analysis in cases where the same term can belong to multiple categories. Two IFS similarity measure-based techniques, namely, the Intuitionistic Fuzzy Similarity Model for Developer Term Relation (IFSDTR) and the Intuitionistic Fuzzy Similarity Model for Developer Category Relation (IFSDCR), are proposed in this work. In IFSDTR, a developer -term vocabulary is constructed based on the previous bug-fixing experience of software developers by considering the most frequent terms in the IFS representation of bugs they fixed earlier. In IFSDCR, soft-ware bugs are categorized into multiple categories and a developer-category relation is constructed. When a new bug is reported, the IFS similarity measure is calculated with the developer-term and developer-category relationship, and a fuzzy a-cut is applied to find a group of expert developers to fix it. The proposed techniques are evaluated on the available data set and compared with existing approaches to bug triaging. On the Eclipse, Mozilla, and NetBeans data sets, the IFSDTR techniques yield an accuracy of 0.90, 0.89, and 0.87, respectively, whereas the IFSDCR yields a greater accuracy of 0.93, 0.90, and 0.88 for the Eclipse, Mozilla, and NetBeans data sets, respectively. Similarly, in all other perfor-mance measures, the proposed approaches outperform the state-of-the-art approaches. (c) 2022 Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).