arXiv subjects

João Paulo Biazotto

Publications and source records attributed to João Paulo Biazotto.

4 recordsLinked to original sources

arXiv2026

TagDebt: A Bot to Support Technical Debt Management

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.

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arXiv2026

Investigating CI/CD-based Technical Debt Management in Open-source Projects

Managing technical debt (TD) is critical to ensure the sustainability of long-term software projects. However, the time and cost involved in technical debt management (TDM) often discourage practitioners from performing this activity consistently. Continuous Integration and Continuous Delivery (CI/CD) pipelines offer an opportunity to support TDM by embedding automated practices directly into the development workflow. Despite this potential, it remains unclear how TDM tools could be integrated into CI/CD pipelines, and we still lack established best practices for this process. To address this problem, the objective of this study is to understand how TDM tools have been used in CI/CD pipelines and also identify potential configuration anti-patterns. To this end, we conducted a large-scale mining software repository (MSR) study on GitHub. In total, we collected around 600,000 Travis CI configuration files and 50,000 supporting scripts, and identified 3,684 pipelines that contain at least one TDM tool. We applied descriptive statistics to analyze the prevalence of tools and anti-patterns, and our findings show that most tools are executed and integrated using an external script; in addition, \textit{Absent Feedback} is the most common configuration anti-pattern. We believe that researchers and practitioners can use the evidence of this study to further investigate how to improve both the tools that are integrated in CI/CD and the integration practices.

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arXiv2025

Automating Technical Debt Management: Insights from Practitioner Discussions in Stack Exchange

Managing technical debt (TD) is essential for maintaining long-term software projects. Nonetheless, the time and cost involved in technical debt management (TDM) are often high, which may lead practitioners to omit TDM tasks. The adoption of tools, and particularly the usage of automated solutions, can potentially reduce the time, cost, and effort involved. However, the adoption of tools remains low, indicating the need for further research on TDM automation. To address this problem, this study aims at understanding which TDM activities practitioners are discussing with respect to automation in TDM, what tools they report for automating TDM, and the challenges they face that require automation solutions. To this end, we conducted a mining software repositories (MSR) study on three websites of Stack Exchange (Stack Overflow, Project Management, and Software Engineering) and collected 216 discussions, which were analyzed using both thematic synthesis and descriptive statistics. We found that identification and measurement are the most cited activities. Furthermore, 51 tools were reported as potential alternatives for TDM automation. Finally, a set of nine main challenges were identified and clustered into two main categories: challenges driving TDM automation and challenges related to tool usage. These findings highlight that tools for automating TDM are being discussed and used; however, several significant barriers persist, such as tool errors and poor explainability, hindering the adoption of these tools. Moreover, further research is needed to investigate the automation of other TDM activities such as TD prioritization.

cs.SE↗

arXiv2023

Technical Debt Management Automation: State of the Art and Future Perspectives

Technical Debt (TD) refers to non-optimal decisions made in software projects that may lead to short-term benefits, but potentially harm the system's maintenance in the long-term. Technical debt management (TDM) refers to a set of activities that are performed to handle TD, e.g., identification. These activities can entail tasks such as code and architectural analysis, which can be time-consuming if done manually. Thus, substantial research work has focused on automating TDM tasks (e.g., automatic identification of code smells). However, there is a lack of studies that summarize current approaches in TDM automation. This can hinder practitioners in selecting optimal automation strategies to efficiently manage TD. It can also prevent researchers from understanding the research landscape and addressing the research problems that matter the most. Thus, the main objective of this study is to provide an overview of the state of the art in TDM automation, analyzing the available tools, their use, and the challenges in automating TDM. For this, we conducted a systematic mapping study (SMS), and from an initial set of 1086 primary studies, 178 were selected to answer three research questions covering different facets of TDM automation. We found 121 automation artifacts, which were classified in 4 different types (i.e., tools, plugins, scripts, and bots); the inputs/outputs and interfaces were also collected and reported. Finally, a conceptual model is proposed that synthesizes the results and allows to discuss the current state of TDM automation and related challenges. The results show that the research community has investigated to a large extent how to perform various TDM activities automatically, considering the number of studies and automation artifacts we identified. More research is needed towards fully automated TDM, specially concerning the integration of the automation artifacts.

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