Paris Avgeriou

Edi Sutoyo, Paris Avgeriou, Andrea Capiluppi

Technical Debt (TD) refers to the long-term costs incurred when developers prioritize short-term delivery over quality-improving work. Architectural Technical Debt (ATD) arises when architectural decisions (e.g., technology choices, patterns, or decomposition) prioritize near-term progress over future maintainability and evolvability. Because ATD affects a system's core structure and propagates through architectural dependencies, it is often more expensive and disruptive to remediate than localized code-level debt. Although ATD has been widely studied, an important but underexplored aspect of repayment is who performs it. Prior work provides limited empirical evidence on repayment responsibility in ATD and its relationship to time-to-fix. We empirically study self-fixed ATD, where the introducer also repays the debt, and contrast it with non-self-fixed ATD in large Apache open-source projects. We reconstruct ATD lifecycles by tracing Jira artifacts to version-control history to identify introduction and repayment points and attribute developer roles. We address three research questions on the prevalence of self-fixed ATD, time-to-fix differences between self-fixed and non--self-fixed items, and how factors related to code change and collaboration metrics relate to repayment speed. Using descriptive statistics, non-parametric tests, and survival analysis, we show that self-fixed and non--self-fixed ATD exhibit distinct repayment dynamics and differences in how changes are shared on ATD-affected files. In particular, non--self-fixed ATD is more likely to remain unresolved longer when changes are spread across many developers. These results provide actionable guidance for maintainers to identify high-risk ATD items and to reduce handoff costs by increasing introducer involvement when possible and documenting the design rationale during repayment.

João Paulo Biazotto, Daniel Feitosa, Paris Avgeriou et al.

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.