Industrial applications of software defect prediction using machine learning: A business-driven systematic literature review

Context: Machine learning software defect prediction is a promising field of software engineering, attracting a great deal of attention from the research community; however, its industry application tents to lag behind academic achievements.Objective: This study is part of a larger project focused on improving the quality and minimising the cost of software testing of the 5G system at Nokia, and aims to evaluate the business applicability of machine learning software defect prediction and gather lessons learnt.Methods: The systematic literature review was conducted on journal and conference papers published between 2015 and 2022 in popular online databases (ACM, IEEE, Springer, Scopus, Science Direct, and Google Scholar). A quasi-gold standard procedure was used to validate the search, and SEGRESS guidelines were used for transparency, reporting, and replicability.Results: We have selected and analysed 32 publications out of 397 found by our automatic search (and seven by snowballing). We have identified highly relevant evidence of methods, features, frameworks, and datasets used. However, we found a minimal emphasis on practical lessons learnt and cost consciousness - both vital from a business perspective.Conclusion: Even though the number of machine learning software defect prediction studies validated in the industry is increasing (and we were able to identify several excellent papers on studies performed in vivo), there is still not enough practical focus on the business aspects of the effort that would help bridge the gap between the needs of the industry and academic research.