DebtViz: A Tool for Identifying, Measuring, Visualizing, and Monitoring Self-Admitted Technical Debt (2024)

Abstract

Technical debt, specifically Self-Admitted Technical Debt (SATD), remains a significant challenge for software developers and managers due to its potential to adversely affect long-Term software maintainability. Although various approaches exist to identify SATD, tools for its comprehensive management are notably lacking. This paper presents DebtViz, an innovative SATD tool designed to automatically detect, classify, visualize and monitor various types of SATD in source code comments and issue tracking systems. DebtViz employs a Convolutional Neural Network-based approach for detection and a deconvolution technique for keyword extraction. The tool is structured into a back-end service for data collection and pre-processing, a SATD classifier for data categorization, and a front-end module for user interaction. DebtViz not only makes the management of SATD more efficient but also provides in-depth insights into the state of SATD within software systems, fostering informed decision-making on managing it. The scalability and deployability of DebtViz also make it a practical tool for both developers and managers in diverse software development environments. The source code of DebtViz is available at https://github.com/yikun-li/visdom-satd-management-system and the demo of DebtViz is at https://youtu.be/QXH6Bj0HQew.

Original language	English
Title of host publication	Proceedings - 2023 IEEE International Conference on Software Maintenance and Evolution, ICSME 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	558-562
Number of pages	5
ISBN (Electronic)	9798350327830
DOIs	https://doi.org/10.1109/ICSME58846.2023.00072
Publication status	Published - Dec-2023
Event	39th IEEE International Conference on Software Maintenance and Evolution, ICSME 2023 - Bogota, Colombia Duration: 1-Oct-2023 → 6-Oct-2023

Publication series

Name	Proceedings - 2023 IEEE International Conference on Software Maintenance and Evolution, ICSME 2023

Conference

Conference	39th IEEE International Conference on Software Maintenance and Evolution, ICSME 2023
Country/Territory	Colombia
City	Bogota
Period	01/10/2023 → 06/10/2023

Keywords

self-Admitted technical debt
technical debt management
technical debt visualization

Access to Document

10.1109/ICSME58846.2023.00072

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Li, Y., Soliman, M., Avgeriou, P., & Van Ittersum, M. (2023). DebtViz: A Tool for Identifying, Measuring, Visualizing, and Monitoring Self-Admitted Technical Debt. In Proceedings - 2023 IEEE International Conference on Software Maintenance and Evolution, ICSME 2023 (pp. 558-562). (Proceedings - 2023 IEEE International Conference on Software Maintenance and Evolution, ICSME 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSME58846.2023.00072

Li, Yikun ; Soliman, Mohamed ; Avgeriou, Paris et al. / DebtViz : A Tool for Identifying, Measuring, Visualizing, and Monitoring Self-Admitted Technical Debt. Proceedings - 2023 IEEE International Conference on Software Maintenance and Evolution, ICSME 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 558-562 (Proceedings - 2023 IEEE International Conference on Software Maintenance and Evolution, ICSME 2023).

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title = "DebtViz: A Tool for Identifying, Measuring, Visualizing, and Monitoring Self-Admitted Technical Debt",

abstract = "Technical debt, specifically Self-Admitted Technical Debt (SATD), remains a significant challenge for software developers and managers due to its potential to adversely affect long-Term software maintainability. Although various approaches exist to identify SATD, tools for its comprehensive management are notably lacking. This paper presents DebtViz, an innovative SATD tool designed to automatically detect, classify, visualize and monitor various types of SATD in source code comments and issue tracking systems. DebtViz employs a Convolutional Neural Network-based approach for detection and a deconvolution technique for keyword extraction. The tool is structured into a back-end service for data collection and pre-processing, a SATD classifier for data categorization, and a front-end module for user interaction. DebtViz not only makes the management of SATD more efficient but also provides in-depth insights into the state of SATD within software systems, fostering informed decision-making on managing it. The scalability and deployability of DebtViz also make it a practical tool for both developers and managers in diverse software development environments. The source code of DebtViz is available at https://github.com/yikun-li/visdom-satd-management-system and the demo of DebtViz is at https://youtu.be/QXH6Bj0HQew.",

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author = "Yikun Li and Mohamed Soliman and Paris Avgeriou and {Van Ittersum}, Maarten",

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Li, Y, Soliman, M, Avgeriou, P & Van Ittersum, M 2023, DebtViz: A Tool for Identifying, Measuring, Visualizing, and Monitoring Self-Admitted Technical Debt. in Proceedings - 2023 IEEE International Conference on Software Maintenance and Evolution, ICSME 2023. Proceedings - 2023 IEEE International Conference on Software Maintenance and Evolution, ICSME 2023, Institute of Electrical and Electronics Engineers Inc., pp. 558-562, 39th IEEE International Conference on Software Maintenance and Evolution, ICSME 2023, Bogota, Colombia, 01/10/2023. https://doi.org/10.1109/ICSME58846.2023.00072

DebtViz: A Tool for Identifying, Measuring, Visualizing, and Monitoring Self-Admitted Technical Debt. / Li, Yikun; Soliman, Mohamed; Avgeriou, Paris et al.
Proceedings - 2023 IEEE International Conference on Software Maintenance and Evolution, ICSME 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 558-562 (Proceedings - 2023 IEEE International Conference on Software Maintenance and Evolution, ICSME 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Li Y, Soliman M, Avgeriou P, Van Ittersum M. DebtViz: A Tool for Identifying, Measuring, Visualizing, and Monitoring Self-Admitted Technical Debt. In Proceedings - 2023 IEEE International Conference on Software Maintenance and Evolution, ICSME 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 558-562. (Proceedings - 2023 IEEE International Conference on Software Maintenance and Evolution, ICSME 2023). doi: 10.1109/ICSME58846.2023.00072

As an expert in software engineering and technical debt management, I've been deeply involved in researching and implementing strategies to mitigate technical debt in software projects. Technical debt, especially Self-Admitted Technical Debt (SATD), poses a significant challenge for developers and managers alike, impacting the long-term maintainability and scalability of software systems.

The concept of SATD refers to the kind of technical debt that developers acknowledge within the codebase, often through comments or documentation. It serves as an indicator of areas in the code that need improvement or refactoring. SATD, if left unmanaged, can accumulate over time, leading to increased development costs and decreased system stability.

In the article you provided, the authors introduce DebtViz, an innovative tool designed to address the challenges associated with managing SATD effectively. Here are the key concepts related to DebtViz and the management of SATD:

Self-Admitted Technical Debt (SATD):
- SATD refers to technical debt that developers acknowledge within the codebase through comments or documentation. It highlights areas in the code that require attention or refactoring.
Technical Debt Management:
- Technical debt management involves identifying, prioritizing, and mitigating technical debt within a software project. Effective management strategies help maintain the long-term health and maintainability of the codebase.
Technical Debt Visualization:
- Visualization techniques aid in understanding the distribution and impact of technical debt within the codebase. Visual representations can help developers and managers make informed decisions about where to allocate resources for debt reduction.
DebtViz:
- DebtViz is a tool specifically designed for identifying, measuring, visualizing, and monitoring Self-Admitted Technical Debt (SATD) within software projects.
- It employs a Convolutional Neural Network-based approach for SATD detection and a deconvolution technique for keyword extraction from source code comments and issue tracking systems.
- DebtViz comprises a structured architecture including a back-end service for data collection and pre-processing, a SATD classifier for data categorization, and a front-end module for user interaction.
- The tool aims to make the management of SATD more efficient and provides insights into the state of SATD within software systems, enabling informed decision-making.
- DebtViz is scalable and deployable, making it practical for use in diverse software development environments.
Availability:
- The source code of DebtViz is available on GitHub (), and a demo of DebtViz can be accessed at .

By leveraging tools like DebtViz and adopting best practices in technical debt management, software development teams can effectively address SATD and ensure the long-term maintainability and sustainability of their projects.