# Deep Learning-based Software Engineering: Progress, Challenges, and Opportunities

arXiv: https://arxiv.org/abs/2410.13110 · HTML: https://mlsanity.com/paper/2410.13110/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2024.

## Built on by (most cited first, 9 of 9)

- [CodeV: Issue Resolving with Visual Data](https://mlsanity.com/paper/2412.17315/) (2024)
- [The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Project](https://mlsanity.com/paper/2511.01348/) (2025)
- [FastCoder: Accelerating Repository-level Code Generation via Efficient Retrieval and Verification](https://mlsanity.com/paper/2502.17139/) (2025)
- [A Comprehensive Study of Deep Learning Model Fixing Approaches](https://mlsanity.com/paper/2512.23745/) (2025)
- [TraceDev: A Traceability-Driven Multi-agent Framework for Requirement-to-Code Development](https://mlsanity.com/paper/2607.18886/) (2026)
- [SAP-DIFF: Semantic Adversarial Patch Generation for Black-Box Face Recognition Models via Diffusion Models](https://mlsanity.com/paper/2502.19710/) (2025)
- [Wired for Reuse: Automating Context-Aware Code Adaptation in IDEs via LLM-Based Agent](https://mlsanity.com/paper/2507.01315/) (2025)
- [A Defect Classification Framework for AI-Based Software Systems (AI-ODC)](https://mlsanity.com/paper/2508.17900/) (2025)
- [GourNet: A CNN-Based Model for Mango Leaf Disease Detection](https://mlsanity.com/paper/2604.27764/) (2026)

## Abstract (arXiv)

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many papers have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However, although several surveys have provided overall pictures of the application of deep learning techniques in software engineering, they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this paper, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out the through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically.