分析2015-2023年GitHub问题数据，追踪技术债主题随时间、语言和仓库的变化。
Unravelling Technical debt topics through Time, Programming Languages and Repository
- 用BERTopic对GitHub问题进行主题建模，识别技术债主题
- 发现技术债主题随时间演变，不同语言和仓库差异显著
- 结合情感分析，揭示开发者对各类技术债的态度变化
本研究通过分析2015年至2023年9月的GitHub问题数据，探索软件工程中技术债（TD）主题的动态演变。尽管已有大量研究关注技术债的识别与量化，但对其主题多样性及时间发展仍缺乏深入理解。为此，我们采用BERTopic进行主题建模，对技术债主题进行分类并追踪其历时演变。同时，对每个主题引入情感分析，揭示相关开发者的感知与态度。结果表明，技术债主题在时间、编程语言和仓库间存在明显差异，为理解技术债的演化趋势提供了更细致的视角。
原文摘要 · Abstract (English)
This study explores the dynamic landscape of Technical Debt (TD) topics in software engineering by examining its evolution across time, programming languages, and repositories. Despite the extensive research on identifying and quantifying TD, there remains a significant gap in understanding the diversity of TD topics and their temporal development. To address this, we have conducted an explorative analysis of TD data extracted from GitHub issues spanning from 2015 to September 2023. We employed BERTopic for sophisticated topic modelling. This study categorises the TD topics and tracks their progression over time. Furthermore, we have incorporated sentiment analysis for each identified topic, providing a deeper insight into the perceptions and attitudes associated with these topics. This offers a more nuanced understanding of the trends and shifts in TD topics through time, programming language, and repository.
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