Unravelling Technical debt topics through Time, Programming Languages and Repository

• URL: http://arxiv.org/abs/2504.11714v1
• Date: Wed, 16 Apr 2025 02:20:56 GMT
• Title: Unravelling Technical debt topics through Time, Programming Languages and Repository
• Authors: Karthik Shivashankar, Antonio Martini,
• Abstract summary: This study explores the dynamic landscape of Technical Debt (TD) topics in software engineering by examining its evolution across time, programming languages, and repositories.<n>We have conducted an explorative analysis of TD data extracted from GitHub issues spanning from 2015 to September 2023.<n>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.
• Score: 5.669063174637433
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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