# Unravelling Technical debt topics through Time, Programming Languages and Repository

arXiv: https://arxiv.org/abs/2504.11714 · HTML: https://mlsanity.com/paper/2504.11714/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2109.08306/) (2021)
- [Experiments on Generalizability of BERTopic on Multi-Domain Short Text](https://mlsanity.com/paper/2212.08459/) (2022)
- [A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts](https://mlsanity.com/paper/2205.04185/) (2022)
- [A Statistical Parsing Framework for Sentiment Classification](https://mlsanity.com/paper/1401.6330/) (2014)
- [Exploring the Power of Topic Modeling Techniques in Analyzing Customer Reviews: A Comparative Analysis](https://mlsanity.com/paper/2308.11520/) (2023)
- [A Scalable, Lexicon Based Technique for Sentiment Analysis](https://mlsanity.com/paper/1410.2265/) (2014)
- [Does Technical Debt Lead to the Rejection of Pull Requests?](https://mlsanity.com/paper/1604.01450/) (2016)
- [Assuring the Evolvability of Microservices: Insights into Industry Practices and Challenges](https://mlsanity.com/paper/1906.05013/) (2019)
- [ScenarioSA: A Large Scale Conversational Database for Interactive Sentiment Analysis](https://mlsanity.com/paper/1907.05562/) (2019)
- [Predicting Community Smells' Occurrence on Individual Developers by Sentiments](https://mlsanity.com/paper/2103.07090/) (2021)

## 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.