Problem
Research questions and friction points this paper is trying to address.
technical debt
self-admitted technical debt
technical debt management
tool integration
software maintainability
TagDebt: A Bot to Support Technical Debt Management
🤖 AI Summary
This study addresses the lack of lightweight, seamlessly integrated tools for managing technical debt in current software development workflows, particularly for efficiently identifying self-admitted technical debt (SATD). Drawing on design science research methodology, the authors developed and evaluated TagDebt—an automated bot integrated into GitHub that dynamically labels issues with SATD tags in real time, enabling unobtrusive debt detection within developers’ existing workflows. An evaluation involving semi-structured interviews with 16 practitioners, grounded in the Technology Acceptance Model, demonstrated TagDebt’s effectiveness: participants consistently perceived it as useful and easy to use, noting substantial reductions in manual classification effort and improved issue organization. The study also revealed that team size and repository context significantly influence adoption intentions. To the best of the authors’ knowledge, this work presents the first lightweight, automated SATD tagging system embedded directly within a developer collaboration platform.
📝 Abstract
Context: Technical debt (TD) is a widely studied metaphor that helps to explain how sub-optimal decisions that can harm software maintainability over time. Although incurring TD is not intrinsically bad, tracking and managing TD are crucial to avoid its negative effects. Hence, researchers and practitioners have proposed and developed diverse approaches and tools for managing TD. However, we are still lacking specialized tools for technical debt management (TDM), specifically ones that can be easily integrated into existing development workflows. Objective: We present and evaluate TagDebt, a bot that can be integrated within GitHub repositories and automatically assign labels to issues (i.e., SATD or non-SATD). TagDebt helps in the identification of TD (i.e., by looking for self-admitted technical debt (SATD)), leading to more efficient TDM. Methods: We carried out a Design Science Research study to design and implement TagDebt. For its evaluation, we executed a Technology Acceptance Model (TAM) study through interviews with 16 practitioners, to check the bot's usefulness, ease of use, and contextual factors that might impact the bot's usage (such as team size and practitioners' roles). Results: Overall, practitioners found that TagDebt is useful, especially for organizing issues and reducing manual work. Furthermore, they pointed out that the bot is overall easy to use, and its documentation is clear. The analysis also revealed that contextual factors, such as team and codebase size, impact the decision to adopt TagDebt. Finally, several improvements were suggested, such as including features to check and update the source code. Conclusion: TagDebt is a proof-of-concept for the development and usage of more specialized tools for TDM. It helps to make TD visible without disrupting existing workflows and help practitioners avoid the risks of unmanaged TD.
Innovation
Methods, ideas, or system contributions that make the work stand out.
technical debt management
self-admitted technical debt
GitHub bot
Design Science Research
Technology Acceptance Model
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