Enhancing Debugging Skills with AI-Powered Assistance: A Real-Time Tool for Debugging Support

Elizaveta Artser, Daniil Karol, Anna Potriasaeva, Aleksei Rostovskii, Katsiaryna Dzialets, Ekaterina Koshchenko, Xiaotian Su, April Yi Wang, and Anastasiia Birillo

April, 2026. Accepted to ICSE'26 (A*).

Abstract. Debugging is a crucial skill in programming education and software development, yet it is often overlooked in CS curricula. To address this, we introduce an AI-powered debugging assistant integrated into an IDE. It offers real-time support by analyzing code, suggesting breakpoints, and providing contextual hints. Using RAG with LLMs, program slicing, and custom heuristics, it enhances efficiency by minimizing LLM calls and improving accuracy. A three-level evaluation - technical analysis, UX study, and classroom tests - highlights its potential for teaching debugging.