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The Dual-Edged Sword of Technical Debt: Benefits and Issues Analyzed Through Developer Discussions

Xiaozhou Li, Matteo Esposito, Andrea Janes, Valentina Lenarduzzi

TL;DR

This study addresses how software practitioners perceive technical debt (TD) by mining a large corpus of grey literature from StackOverflow, Medium, and DZone. It employs a six-step NLP pipeline—preprocessing, informativeness filtering, GSDMM topic modeling, topic filtering, topic interpretation with AI-assisted summarization, and VADER-based sentiment analysis—to identify eight main TD topics and 43 subtopics, along with the positive/negative sentiment associated with each. The results reveal key practitioner concerns (e.g., unclear roles, engagement gaps) and benefits (e.g., team collaboration, prioritization), and show varying sentiment across topics, with business-impact and TD management often viewed negatively while certain pragmatic TD-management practices are viewed positively. The paper contributes a replicable framework for aggregating practitioner opinions from grey literature, highlights practical implications for agile and DevOps contexts, and proposes AI-driven TD detection and management as a fruitful direction for future work.

Abstract

Background. Technical debt (TD) has long been one of the key factors influencing the maintainability of software products. It represents technical compromises that sacrifice long-term software quality for potential short-term benefits. Objective. This work is to collectively investigate the practitioners' opinions on the various perspectives of TD from a large collection of articles. We find the topics and latent details of each, where the sentiments of the detected opinions are also considered. Method. For such a purpose, we conducted a grey literature review on the articles systematically collected from three mainstream technology forums. Furthermore, we adopted natural language processing techniques like topic modeling and sentiment analysis to achieve a systematic and comprehensive understanding. However, we adopted ChatGPT to support the topic interpretation. Results. In this study, 2,213 forum posts and articles were collected, with eight main topics and 43 sub-topics identified. For each topic, we obtained the practitioners' collective positive and negative opinions. Conclusion. We identified 8 major topics in TD related to software development. Identified challenges by practitioners include unclear roles and a lack of engagement. On the other hand, active management supports collaboration and mitigates the impact of TD on the source code.

The Dual-Edged Sword of Technical Debt: Benefits and Issues Analyzed Through Developer Discussions

TL;DR

This study addresses how software practitioners perceive technical debt (TD) by mining a large corpus of grey literature from StackOverflow, Medium, and DZone. It employs a six-step NLP pipeline—preprocessing, informativeness filtering, GSDMM topic modeling, topic filtering, topic interpretation with AI-assisted summarization, and VADER-based sentiment analysis—to identify eight main TD topics and 43 subtopics, along with the positive/negative sentiment associated with each. The results reveal key practitioner concerns (e.g., unclear roles, engagement gaps) and benefits (e.g., team collaboration, prioritization), and show varying sentiment across topics, with business-impact and TD management often viewed negatively while certain pragmatic TD-management practices are viewed positively. The paper contributes a replicable framework for aggregating practitioner opinions from grey literature, highlights practical implications for agile and DevOps contexts, and proposes AI-driven TD detection and management as a fruitful direction for future work.

Abstract

Background. Technical debt (TD) has long been one of the key factors influencing the maintainability of software products. It represents technical compromises that sacrifice long-term software quality for potential short-term benefits. Objective. This work is to collectively investigate the practitioners' opinions on the various perspectives of TD from a large collection of articles. We find the topics and latent details of each, where the sentiments of the detected opinions are also considered. Method. For such a purpose, we conducted a grey literature review on the articles systematically collected from three mainstream technology forums. Furthermore, we adopted natural language processing techniques like topic modeling and sentiment analysis to achieve a systematic and comprehensive understanding. However, we adopted ChatGPT to support the topic interpretation. Results. In this study, 2,213 forum posts and articles were collected, with eight main topics and 43 sub-topics identified. For each topic, we obtained the practitioners' collective positive and negative opinions. Conclusion. We identified 8 major topics in TD related to software development. Identified challenges by practitioners include unclear roles and a lack of engagement. On the other hand, active management supports collaboration and mitigates the impact of TD on the source code.
Paper Structure (20 sections, 5 figures, 2 tables)

This paper contains 20 sections, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Study Design
  3. Goal and Research Questions
  4. Data and Processing
  5. Keyword selection
  6. Data Source selection
  7. Data Extraction
  8. Data Analysis
  9. Verifiability and Replicability
  10. Results
  11. Step 1: Preprocessing.
  12. Step 2: Informativeness Filtering.
  13. Step 3: Topic Modeling.
  14. Step 4: Topic Filtering.
  15. Step 5: Topic Interpretation and Mapping.
  16. ...and 5 more sections

Figures (5)

  • Figure 1: Overview of the followed process (adapted from janes2023open)
  • Figure 2: Data Analysis process (adapted from janes2023open)
  • Figure 3: Testing informative text classifier accuracy
  • Figure 4: Topic Sentiment Summary
  • Figure 5: Sentiment Distribution for the Sub-Topics