  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.
