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

TL;DR

DebtViz is a comprehensive tool that automatically detects, classifies, visualizes, and monitors Self-Admitted Technical Debt in source code comments and issue systems, aiding long-term software maintainability.

Contribution

It introduces a novel, scalable, and deployable SATD management tool employing CNNs and deconvolution for detection and keyword extraction, filling a gap in existing tools.

Findings

Effective detection and classification of SATD in source code comments.

Provides visualizations and monitoring features for SATD management.

Open-source implementation available for practical use.

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.