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This paper introduces TagDebt, a GitHub bot designed to automatically label issues as related to self-admitted technical debt (SATD) or not, aiming to improve technical debt management (TDM). The bot was evaluated using a Technology Acceptance Model (TAM) study involving interviews with 16 practitioners to assess its usefulness and ease of use. Results indicate that practitioners found TagDebt useful for organizing issues and reducing manual effort, though contextual factors like team size influence adoption.
Stop manually sifting through GitHub issues for technical debt – this bot automates the process, making it easier to spot and manage.
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.
