Application of metaheuristic techniques in software quality prediction: a systematic mapping study. (6th January 2022) - Lakra, Kirti; Chug, Anuradha
This paper focuses on the systematic review of various metaheuristic techniques employed for analysing different software quality aspects, including fault proneness, defect anticipation, change proneness, maintainability prediction, and software reliability prediction. It is observed that machine learning algorithms are still popular models, but metaheuristic algorithms are also gaining popularity in the field of software quality measurement. This is due to the fact that metaheuristic algorithms are more efficient in solving real-world, search-based, and optimisation problems. Initially, 90 papers were considered and analysed for conducting this study from 2010 to 2020, and 55 studies were shortlisted based on predesigned quality evaluation standards. Resultantly, particle swarm optimisation (PSO), and genetic algorithms came out as the most prominently used metaheuristic techniques for developing software quality models in 36.3% and 27.2% of the shortlisted studies, respectively. The current review will benefit other researchers by providing an insight into the current trends in software quality domain.