Change Impact Analysis Using Machine Learning: A Systematic Literature Review

Abstract

Change Impact Analysis (CIA) and requirement change propagation are increasingly critical in complex software systems, particularly in the context of information reuse and integration for data science. Currently, CIA is predominantly conducted manually with the support of domain experts. This approach is often time-consuming and susceptible to human error, especially in overlooking affected requirements. Leveraging data science and machine learning (ML) offers promising opportunities to automate and enhance CIA by enabling intelligent prediction and reuse of previously analyzed change patterns. These predictive models can assist experts by highlighting the likely affected requirements, improving accuracy, and reducing effort. This paper explores the current state of CIA practices, evaluates existing ML-based solutions aimed at enhancing the process, and examines their effectiveness, limitations, and opportunities for improvement. We conducted a systematic literature review (SLR) guided by three primary research questions, analyzing 43 studies published over the past two decades. Our findings highlight a lack of collaboration between academia and industry, as well as the limited credibility of existing tools and methods, largely due to the scarcity of comprehensive and diverse datasets.