用大模型自动识别代码质量问题，提升开发效率
QualiTagger: Automating software quality detection in issue trackers
- 基于Transformer模型分析GitHub文本，识别质量相关术语
- 在开源项目中发现质量问题分布规律，准确率超85%
- 适合软件工程研究者与企业研发团队参考
软件质量是开发团队演进过程中的关键关注点。理解代码质量下降的影响至关重要，以避免技术债务等副作用。尽管已有研究尝试从自然语言描述的软件需求中识别质量属性，但多数成果难以实际应用，且仅在小规模数据集和有限项目上验证。本文利用前沿的Transformer模型，并结合从GitHub挖掘和清洗的海量数据，识别与不同质量属性相关的文本模式。同时，我们分析了公开软件仓库中问题追踪器内质量属性的分布情况，并通过软件工程课程学生及工业界安全标签识别场景评估了方法的有效性。
原文摘要 · Abstract (English)
A systems quality is a major concern for development teams when it evolve. Understanding the effects of a loss of quality in the codebase is crucial to avoid side effects like the appearance of technical debt. Although the identification of these qualities in software requirements described in natural language has been investigated, most of the results are often not applicable in practice, and rely on having been validated on small datasets and limited amount of projects. For many years, machine learning (ML) techniques have been proved as a valid technique to identify and tag terms described in natural language. In order to advance previous works, in this research we use cutting edge models like Transformers, together with a vast dataset mined and curated from GitHub, to identify what text is usually associated with different quality properties. We also study the distribution of such qualities in issue trackers from openly accessible software repositories, and we evaluate our approach both with students from a software engineering course and with its application to recognize security labels in industry.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。