Applying Machine Learning in Technical Debt Management: Future Opportunities and Challenges

Technical Debt Management (TDM) is a fast-growing field that in the last years has attracted the attention of both academia and industry. TDM is a complex process, in the sense that it relies on multiple and heterogeneous data sources (e.g., source code, feature requests, bugs, developers’ activity, etc.), which cannot be straightforwardly synthesized; leading the community to using mostly qualitative empirical methods. However, empirical studies that involve expert judgement are inherently biased, compared to automated or semi-automated approaches. To overcome this limitation, the broader (not TDM) software engineering community has started to employ machine learning (ML) technologies. Our goal is to investigate the opportunity of applying ML technologies for TDM, through a Systematic Literature Review (SLR) on the application of ML to software engineering problems (since ML applications on TDM are limited). Thus, we have performed a broader scope study, i.e., on machine learning for software engineering, and then synthesize the results so as to achieve our high-level goal (i.e., possible application of ML in TDM). Therefore, we have conducted a literature review, by browsing the research corpus published in five high-quality SE journals, with the goal of cataloging: (a) the software engineering practices in which ML is used; (b) the machine learning technologies that are used for solving them; and (c) the intersection of the two: developing a problem-solution mapping. The results are useful to both academics and industry, since the former can identify possible gaps, and interesting future research directions, whereas the latter can obtain benefits by adopting ML technologies.