Deep Learning in Software Engineering

TL;DR

This paper reviews how deep learning is integrated into Software Engineering, analyzing 98 studies to identify its applications, benefits, and challenges across SE phases, highlighting the need for more practical and effective solutions.

Contribution

It provides a comprehensive bibliography analysis of deep learning applications in SE, identifying the tasks facilitated and highlighting current limitations and future research directions.

Findings

41 SE tasks facilitated by deep learning

84.7% use standard deep learning models

Practicality and effectiveness are key future concerns

Abstract

Recent years, deep learning is increasingly prevalent in the field of Software Engineering (SE). However, many open issues still remain to be investigated. How do researchers integrate deep learning into SE problems? Which SE phases are facilitated by deep learning? Do practitioners benefit from deep learning? The answers help practitioners and researchers develop practical deep learning models for SE tasks. To answer these questions, we conduct a bibliography analysis on 98 research papers in SE that use deep learning techniques. We find that 41 SE tasks in all SE phases have been facilitated by deep learning integrated solutions. In which, 84.7% papers only use standard deep learning models and their variants to solve SE problems. The practicability becomes a concern in utilizing deep learning techniques. How to improve the effectiveness, efficiency, understandability, and testability of deep learning based solutions may attract more SE researchers in the future.