Utilizing Deep Learning to Optimize Software Development Processes
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917656178720768 |
|---|---|
| author | Li, Keqin Zhu, Armando Zhao, Peng Song, Jintong Liu, Jiabei |
| author_facet | Li, Keqin Zhu, Armando Zhao, Peng Song, Jintong Liu, Jiabei |
| contents | This study explores the application of deep learning technologies in software development processes, particularly in automating code reviews, error prediction, and test generation to enhance code quality and development efficiency. Through a series of empirical studies, experimental groups using deep learning tools and control groups using traditional methods were compared in terms of code error rates and project completion times. The results demonstrated significant improvements in the experimental group, validating the effectiveness of deep learning technologies. The research also discusses potential optimization points, methodologies, and technical challenges of deep learning in software development, as well as how to integrate these technologies into existing software development workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13630 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Utilizing Deep Learning to Optimize Software Development Processes Li, Keqin Zhu, Armando Zhao, Peng Song, Jintong Liu, Jiabei Software Engineering Artificial Intelligence Computation and Language Machine Learning This study explores the application of deep learning technologies in software development processes, particularly in automating code reviews, error prediction, and test generation to enhance code quality and development efficiency. Through a series of empirical studies, experimental groups using deep learning tools and control groups using traditional methods were compared in terms of code error rates and project completion times. The results demonstrated significant improvements in the experimental group, validating the effectiveness of deep learning technologies. The research also discusses potential optimization points, methodologies, and technical challenges of deep learning in software development, as well as how to integrate these technologies into existing software development workflows. |
| title | Utilizing Deep Learning to Optimize Software Development Processes |
| topic | Software Engineering Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2404.13630 |