Abstract
Detecting software bugs is considered to be an active area of research as bugs detected after the delivery of software is considered to be very expensive to deal with. Whenever a new software bug is detected, software developer faces extreme difficulty in detecting the exact location of the bug in the product. Identifying bugs efficiently through genetic algorithms is the active area of research nowadays. Based on the bug reports dataset, a self-operating genetic algorithm, Strength Pareto Evolutionary Algorithm (SPEA II) identifies and ranks the application files in the reference code as per their likelihood of carrying bugs. In the presented exposition, a text-mining strategy, term frequency–inverse document frequency (TFIDF) is applied for the proper ranking of application files. The ranking is based on the history-based similarity and lexical similarity within the generated report of bugs and the documentation of the application program interface (API). The striking feature of the paper is that a multi-objective approach is used to improve upon the conventional techniques such that contradictory demands of increasing the similarity index and minimizing the number of suggested classes are met simultaneously. Thus, the proposed new approach employs SPEA II as the multi-objective genetic algorithm to meet the conflicting demands and uses both history-based as well as lexical similarity for information retrieval and ranking.
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Sood, A. et al. (2021). Bug Localization Using Multi-objective Approach and Information Retrieval. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1165. Springer, Singapore. https://doi.org/10.1007/978-981-15-5113-0_58
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DOI: https://doi.org/10.1007/978-981-15-5113-0_58
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