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

arXiv: https://arxiv.org/abs/2601.02504 · HTML: https://mlsanity.com/paper/2601.02504/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 5 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation](https://mlsanity.com/paper/2409.20550/) (2024)
- [CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs](https://mlsanity.com/paper/2401.11314/) (2024)
- [CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes](https://mlsanity.com/paper/2308.06921/) (2023)
- [How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging](https://mlsanity.com/paper/2310.05292/) (2023)
- [Evaluating Language Models for Generating and Judging Programming Feedback](https://mlsanity.com/paper/2407.04873/) (2024)
- [Static Analysis as a Feedback Loop: Enhancing LLM-Generated Code Beyond Correctness](https://mlsanity.com/paper/2508.14419/) (2025)
- [Combining Large Language Models with Static Analyzers for Code Review Generation](https://mlsanity.com/paper/2502.06633/) (2025)
- [BugSpotter: Automated Generation of Code Debugging Exercises](https://mlsanity.com/paper/2411.14303/) (2024)
- [Bridging Education and Development: IDEs as Interactive Learning Platforms](https://mlsanity.com/paper/2401.14284/) (2024)
- [One Step at a Time: Combining LLMs and Static Analysis to Generate Next-Step Hints for Programming Tasks](https://mlsanity.com/paper/2410.09268/) (2024)
- [Mapping breakpoint types: an exploratory study](https://mlsanity.com/paper/2109.00917/) (2021)

## Abstract (arXiv)

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.