Data-Driven Software Engineering: A Systematic Literature Review

Over the past few years, emerging technologies have had a significant impact on the processes of software engineering (SE). Consequently, there has been a shift from a more experience-based approach to a data-driven decision-making approach. This shift to data-driven decision-making has resulted in more reliable and accurate decision-making, ultimately leading to more efficient and effective SE processes and a reduction in rework. Our study involved a comprehensive systematic literature review(SLR) examining the utilization of data-driven approaches in SE processes over the last decade. Our analysis of 34 primary studies revealed that data-driven approaches are most commonly utilized. After analyzing the primary studies, we found that data-driven-methods are commonly employed in SE processes for software management and software testing. Researchers are delving into subfields of artificial intelligence, including machine learning and deep learning, to devise decision-making models for SE processes that have undergone extensive validation. We aim to provide valuable insights into the usage of data-driven approaches in SE by conducting a systematic mapping based on the studies that we have found.