[Paper] TagDebt: A Bot to Support Technical Debt Management

Source: arXiv - 2605.29869v1

Overview

The paper introduces TagDebt, a lightweight GitHub bot that automatically tags issues as self‑admitted technical debt (SATD) or not. By surfacing technical debt directly in the issue tracker, the bot aims to make debt management a seamless part of everyday development workflows.

Key Contributions

• TagDebt prototype: An open‑source bot that integrates with GitHub, scans issue titles/descriptions, and applies SATD labels without manual effort.
• Design Science Research (DSR) process: A structured approach that guided the bot’s conception, implementation, and iterative refinement.
• Empirical evaluation using the Technology Acceptance Model (TAM): 16 semi‑structured interviews with practitioners to assess perceived usefulness, ease of use, and contextual adoption factors.
• Insights on adoption drivers: Identification of team size, code‑base magnitude, and role (e.g., developer vs. manager) as key determinants for embracing a TD‑labeling bot.
• Roadmap for future TDM tooling: Concrete suggestions for extending the bot (e.g., linking SATD tags to source‑code locations, automated remediation hints).

Methodology

1. Design Science Research – The authors followed the DSR cycle: problem identification (lack of easy‑to‑integrate TD tools), objectives definition (automatic labeling), design & development (the TagDebt bot), demonstration (deployment on real GitHub repos), and evaluation.
2. Bot implementation – TagDebt listens to GitHub webhook events for new or edited issues, runs a lightweight natural‑language classifier trained on a curated SATD dataset, and posts a label (SATD or non‑SATD).
3. Technology Acceptance Model study – After a pilot rollout, the researchers conducted 16 interviews with developers, team leads, and QA engineers. Participants rated usefulness, ease of use, and reported contextual factors (team size, project age, etc.). Qualitative coding of the interview transcripts produced the findings reported in the paper.

Results & Findings

• Perceived usefulness: 81 % of participants said TagDebt helped them organize technical debt items and reduced the time spent manually triaging issues.
• Ease of use: All respondents found the bot’s setup (adding a GitHub App) straightforward, and the documentation received a “clear and concise” rating.
• Contextual impact: Larger teams and older codebases were more likely to adopt the bot, while very small projects saw less immediate benefit.
• Improvement suggestions: Users requested tighter integration with the code (e.g., linking SATD tags to specific files or lines) and automated reminders for debt repayment.

Practical Implications

• Plug‑and‑play TD visibility: Teams can start surfacing technical debt instantly without altering CI pipelines or issue‑tracking policies.
• Reduced manual overhead: Developers no longer need to remember to label debt; the bot does it consistently, freeing cognitive bandwidth for coding.
• Better prioritization: With SATD tags, product owners and architects can query GitHub for debt‑related issues, feed them into sprint planning tools, or generate debt dashboards.
• Scalable to any GitHub‑hosted project: Because the bot works on issue metadata alone, it can be adopted across micro‑services, open‑source libraries, or monorepos with minimal friction.
• Foundation for richer tooling: The open‑source nature of TagDebt makes it a starting point for extensions such as automated code‑scan hooks, debt‑payment suggestions, or integration with technical‑debt management platforms (e.g., SonarQube, CodeScene).

Limitations & Future Work

• Classification accuracy: The current NLP model, while adequate for the study, can misclassify ambiguous issue texts, leading to false positives/negatives.
• Scope limited to GitHub issues: Projects that track work elsewhere (Jira, Azure Boards) cannot directly benefit without additional adapters.
• No direct code‑level linkage: TagDebt only tags issues; it does not map debt to the exact code locations or suggest remediation steps.
• User sample size: The TAM evaluation involved 16 practitioners, which provides valuable qualitative insight but may not capture broader industry variance.

Future research directions include improving the classifier with deep‑learning models, extending the bot to other issue‑tracking ecosystems, and adding automated code‑analysis hooks that can close the loop between identified SATD and concrete refactoring actions.

Authors

• João Paulo Biazotto
• Daniel Feitosa
• Paris Avgeriou
• Elisa Yumi Nakagawa

Paper Information

• arXiv ID: 2605.29869v1
• Categories: cs.SE
• Published: May 28, 2026
• PDF: Download PDF