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Unravelling Technical debt topics through Time, Programming Languages and Repository

arXiv 2504.11714 ↗First posted 16 Apr 2025cs.SE cs.LGcited by 0

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Abstract (arXiv)

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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References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2021SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysiscited by 23
  3. 2022Experiments on Generalizability of BERTopic on Multi-Domain Short Textcited by 9
  4. 2022A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Textscited by 7
  5. 2014A Statistical Parsing Framework for Sentiment Classificationcited by 5
  6. 2023Exploring the Power of Topic Modeling Techniques in Analyzing Customer Reviews: A Comparative Analysiscited by 2
  7. 2014A Scalable, Lexicon Based Technique for Sentiment Analysiscited by 1
  8. 2016Does Technical Debt Lead to the Rejection of Pull Requests?cited by 1
  9. 2019Assuring the Evolvability of Microservices: Insights into Industry Practices and Challengescited by 1
  10. 2019ScenarioSA: A Large Scale Conversational Database for Interactive Sentiment Analysiscited by 1
  11. 2021Predicting Community Smells' Occurrence on Individual Developers by Sentimentscited by 1

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