Understanding practitioners’ reasoning and requirements for efficient tool support in technical debt management

Abstract Context Maintaining software projects over the long term requires controlling the accumulation of technical debt (TD). However, the time and cost associated with technical debt management (TDM) are often high, hindering practitioners from performing TDM tasks. Using tools for TDM has the potential to reduce the effort involved. Despite this, the adoption of such tools remains low, indicating a need for more efficient tool support. Objective This study aims to understand practitioners’ perspectives on tool support for TDM, specifically regarding the selection and use of these tools. Additionally, we identified potential requirements that could be implemented into existing or new TD tools. Method We surveyed practitioners and received 103 answers, from which 89 valid answers were analyzed using thematic synthesis and descriptive statistics. Results Practitioners’ decision-making processes regarding adopting tools are primarily driven by ten main concerns identified from practitioners’ responses (e.g., the load of information provided by tools). Additionally, we elicited 46 requirements and classified them into two main categories ( “Information to be provided” and “Tool Usage” ). Conclusion Practitioners aim to maintain control over tool execution and outputs. Our study then highlights the necessity of human-centered approaches for TDM automation, i.e., not only tools are essential, but the interaction between tools and practitioners is critical for a more efficient TDM.

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Institutions

Publication Details

Journal
Empirical Software Engineering
Published
2025-07-04
DOI
https://doi.org/10.1007/s10664-025-10691-5
Citations
2
Primary Topic
Software Engineering Research
Type
article
Field-Weighted Citation Impact
6.49

Funders

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article

Understanding practitioners’ reasoning and requirements for efficient tool support in technical debt management

Daniel Feitosa, Elisa Yumi Nakagawa, João Paulo Biazotto, Paris Avgeriou
2 citations
Empirical Software Engineering
Software Engineering Research
6.49
article

Understanding practitioners’ reasoning and requirements for efficient tool support in technical debt management

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

Abstract

Abstract Context Maintaining software projects over the long term requires controlling the accumulation of technical debt (TD). However, the time and cost associated with technical debt management (TDM) are often high, hindering practitioners from performing TDM tasks. Using tools for TDM has the potential to reduce the effort involved. Despite this, the adoption of such tools remains low, indicating a need for more efficient tool support. Objective This study aims to understand practitioners’ perspectives on tool support for TDM, specifically regarding the selection and use of these tools. Additionally, we identified potential requirements that could be implemented into existing or new TD tools. Method We surveyed practitioners and received 103 answers, from which 89 valid answers were analyzed using thematic synthesis and descriptive statistics. Results Practitioners’ decision-making processes regarding adopting tools are primarily driven by ten main concerns identified from practitioners’ responses (e.g., the load of information provided by tools). Additionally, we elicited 46 requirements and classified them into two main categories ( “Information to be provided” and “Tool Usage” ). Conclusion Practitioners aim to maintain control over tool execution and outputs. Our study then highlights the necessity of human-centered approaches for TDM automation, i.e., not only tools are essential, but the interaction between tools and practitioners is critical for a more efficient TDM.

Empirical Software EngineeringVol. 30(5)
University of Groningen (NL), Institute of Physics (PL), Institute of Mathematics and Computer Science (MD), Brazilian Society of Computational and Applied Mathematics (BR)
Fundação de Amparo à Pesquisa do Estado de São Paulo, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Division of Graduate Education
Openalex Percentile: Top 4%
Software Engineering Research
6.49
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