Unravelling Technical debt topics through Time, Programming Languages and Repository
This work addresses a gap in understanding the diversity and temporal development of Technical Debt topics for software engineers, though it is incremental as it applies existing methods to new data.
The study analyzed the evolution of Technical Debt topics in software engineering by examining GitHub issues from 2015 to 2023 using BERTopic for topic modeling and sentiment analysis, categorizing topics and tracking their progression over time, programming languages, and repositories.
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.