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

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 automated 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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Publication Details

Published
2025-04-27
DOI
https://doi.org/10.1109/techdebt66644.2025.00009
Citations
1
Primary Topic
Financial Reporting and XBRL
Type
article
Field-Weighted Citation Impact
2.28
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article

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

Daniel Feitosa, Elisa Yumi Nakagawa, João Paulo Biazotto, Paris Avgeriou
1 citations
Financial Reporting and XBRL
2.28
article

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

Daniel Feitosa, Elisa Yumi Nakagawa, João Paulo Biazotto, Paris Avgeriou
article en
1 citations

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 automated 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.

University of Groningen (NL), Universidade Federal de São Carlos (BR)
Openalex Percentile: Top 11%
Financial Reporting and XBRL
2.28
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Automating Technical Debt Management: Insights from Practitioner Discussions in Stack Exchange — Daniel Feitosa, Elisa Yumi Nakagawa, et al. · (2025) | TGRS Research Map | TGRS