Topic modeling and intuitionistic fuzzy set-based approach for efficient software bug triaging

Modern software development involves multiple developers working remotely in a distributed manner around the world. Software bugs are continuously generated for multiple reasons across various modules. It is possible that one software bug can affect multiple modules, and there can be multiple developers associated with it. Furthermore, many software bug reports are unlabeled, vague, and noisy. The triager faces significant challenges in identifying multiple causes of software bugs and finding expert developers for bug fixing. In this paper, the fuzzy set is extended to Intuitionistic Fuzzy Sets (IFS), and a novel bug triaging approach based on Intuitionistic Fuzzy Similarity (IFSim) measures is presented to overcome the aforementioned problems. The topic model is used to discover multiple relationships between developers and software bugs. IFS is used to separate developers based on their degree of membership and non-membership in a particular software category, with a degree of hesitation for some developers. For a new bug, 15 different IFSim measure techniques are investigated to compute the similarity with the existing software bugs. Finally, a fuzzy alpha-cut is applied to find expert developers to repair it. The best results are obtained by considering the number of topics of 15 and 12 taxonomic terms for each topic. Among all the IFSim measure techniques, the similarity techniques proposed by Ye outperform other techniques. Experiments are carried out on available benchmark data sets, and the results are compared to traditional machine learning algorithms and the fuzzy logic-based Bugzie model.