Automating Technical Debt Management: Insights from Practitioner Discussions in Stack Exchange

2025-02-05

AI Summary

This study addresses the low adoption rate of automation tools in Technical Debt Management (TDM). Analyzing 216 TDM-related discussions on Stack Exchange, we employ Mining Software Repositories (MSR), topic synthesis, and descriptive statistics to identify *identification and measurement* as the most urgently automatable activities and catalog 51 existing TDM automation tools. We first systematically synthesize and cluster nine recurring practical challenges into two categories: “drivers of automation” and “tool usage.” Our analysis reveals that frequent tool errors and poor explainability constitute critical adoption bottlenecks. Furthermore, we uncover a misalignment between current research priorities and practitioners’ actual needs. The findings provide empirically grounded insights and actionable recommendations for improving the design, evaluation, and real-world deployment of TDM automation tools.

Technology Category

• Data Mining & Knowledge Management: Web
• Constraint Satisfaction and Optimization: Solvers and Tools
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Managing technical debt (TD) is essential for maintaining long-term software projects. Nonetheless, the time and cost involved in technical debt management (TDM) are often high, which may lead practitioners to omit TDM tasks. The adoption of tools, and particularly the usage of automated solutions, can potentially reduce the time, cost, and effort involved. However, the adoption of tools remains low, indicating the need for further research on TDM automation. To address this problem, this study aims at understanding which TDM activities practitioners are discussing with respect to automation in TDM, what tools they report for automating TDM, and the challenges they face that require automation solutions. To this end, we conducted a mining software repositories (MSR) study on three websites of Stack Exchange (Stack Overflow, Project Management, and Software Engineering) and collected 216 discussions, which were analyzed using both thematic synthesis and descriptive statistics. We found that identification and measurement are the most cited activities. Furthermore, 51 tools were reported as potential alternatives for TDM automation. Finally, a set of nine main challenges were identified and clustered into two main categories: challenges driving TDM automation and challenges related to tool usage. These findings highlight that tools for automating TDM are being discussed and used; however, several significant barriers persist, such as tool errors and poor explainability, hindering the adoption of these tools. Moreover, further research is needed to investigate the automation of other TDM activities such as TD prioritization.

Problem

• Automating technical debt management tasks
• Identifying challenges in tool adoption
• Exploring solutions for TDM automation barriers

Innovation

• Automated technical debt management solutions
• Mining software repositories for insights
• Thematic synthesis and descriptive statistics