Software Engineering Meets Deep Learning: A Mapping Study
This provides a foundational overview for researchers and practitioners in software engineering, though it is incremental as a survey rather than a novel method.
The paper addresses the lack of summaries at the intersection of deep learning and software engineering by conducting a mapping study of 81 papers, finding that deep learning is increasingly used in SE with top applications in documentation, defect prediction, and testing.
Deep Learning (DL) is being used nowadays in many traditional Software Engineering (SE) problems and tasks. However, since the renaissance of DL techniques is still very recent, we lack works that summarize and condense the most recent and relevant research conducted at the intersection of DL and SE. Therefore, in this paper, we describe the first results of a mapping study covering 81 papers about DL & SE. Our results confirm that DL is gaining momentum among SE researchers over the years and that the top-3 research problems tackled by the analyzed papers are documentation, defect prediction, and testing.