Deep Learning in Software Engineering
May 13, 2018 Β· Declared Dead Β· π arXiv.org
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Authors
Xiaochen Li, He Jiang, Zhilei Ren, Ge Li, Jingxuan Zhang
arXiv ID
1805.04825
Category
cs.SE: Software Engineering
Citations
33
Venue
arXiv.org
Last Checked
4 months ago
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.
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