Software Engineering Meets Deep Learning: A Mapping Study

TL;DR

This paper presents a mapping study of 81 recent research papers, highlighting how deep learning is increasingly applied to software engineering tasks like documentation, defect prediction, and testing.

Contribution

It provides the first comprehensive overview of recent research at the intersection of deep learning and software engineering, identifying key research areas and trends.

Findings

DL is gaining momentum in SE research

Top research problems are documentation, defect prediction, testing

Research volume has increased over the years

Abstract

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.