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

• URL: http://arxiv.org/abs/2502.03153v1
• Date: Wed, 05 Feb 2025 13:23:44 GMT
• Title: Automating Technical Debt Management: Insights from Practitioner Discussions in Stack Exchange
• Authors: João Paulo Biazotto, Daniel Feitosa, Paris Avgeriou, Elisa Yumi Nakagawa,
• Abstract summary: Technical debt management (TDM) is essential for maintaining long-term software projects. The adoption of tools remains low, indicating the need for further research on TDM automation. 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.
• Score: 2.964027141453931
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• 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.

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