DebugTA: An LLM-Based Agent for Simplifying Debugging and Teaching in Programming Education

• URL: http://arxiv.org/abs/2510.11076v1
• Date: Mon, 13 Oct 2025 07:17:18 GMT
• Title: DebugTA: An LLM-Based Agent for Simplifying Debugging and Teaching in Programming Education
• Authors: Lingyue Fu, Haowei Yuan, Datong Chen, Xinyi Dai, Qingyao Li, Weinan Zhang, Weiwen Liu, Yong Yu,
• Abstract summary: In programming and Teaching (DT) task, students receive assistance in correcting their erroneous code.<n>We propose DebugTA, a novel teaching agent with specialized tools for standard code retrieval, variable substitution to align reference code, and an external compiler for real-time code analysis.<n>We show that DebugTA consistently improves teaching effectiveness while significantly reducing computational costs.
• Score: 32.673843958049254
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In programming education, Debugging and Teaching (DT) task is a common scenario where students receive assistance in correcting their erroneous code. The task involves multiple inputs, including erroneous code, error messages, reference solutions, and the question description, with the goal of generating modification suggestions to the erroneous code. However, two key challenges hinder the effectiveness of existing approaches. Firstly, the complexity and heterogeneity of inputs inherent in DT tasks significantly elevate the reasoning challenges faced by LLMs. Second, existing approaches often fail to fully leverage the availability of standard code in DT tasks, forcing models to rely solely on complex multi-step reasoning, which limits the potential of LLMs in addressing DT tasks effectively. To address these challenges, we propose DebugTA, a novel LLM-based debugging and teaching agent with specialized tools for standard code retrieval, variable substitution to align reference code, and an external compiler for real-time code analysis. Guided by explicit pedagogical and debugging principles, DebugTA acts as an agent that decomposes a complex task into sequential LLM interactions, each utilizing distinct tools for specific subtasks, thereby simplifying the logical reasoning at each step and reducing overall reasoning complexity. Furthermore, DebugTA utilizes tool calls to align the standard code with the erroneous code as much as possible, allowing the LLM to focus on logic errors within the erroneous code and improving the accuracy of the generated suggestions. To rigorously assess the quality of modification suggestions, we introduce a student simulator-teacher interaction paradigm. Experimental results on three real-world code datasets demonstrate that DebugTA consistently improves teaching effectiveness while significantly reducing computational costs.

Related papers

• CodeSimpleQA: Scaling Factuality in Code Large Language Models [55.705748501461294]
  We present CodeSimpleQA, a comprehensive benchmark designed to evaluate the factual accuracy of code LLMs in answering code-related questions.<n>We also create CodeSimpleQA-Instruct, a large-scale instruction corpus with 66M samples, and develop a post-training framework combining supervised fine-tuning and reinforcement learning.
  arXiv  Detail & Related papers  (2025-12-22T14:27:17Z)
• Do Code Semantics Help? A Comprehensive Study on Execution Trace-Based Information for Code Large Language Models [24.14163275602762]
  We focus on investigating the usefulness of trace-based semantic information in boosting supervised fine-tuning(SFT) and post-phase inference of Code LLMs.<n>The experimental results surprisingly disagree with previous works and demonstrate that semantic information has limited usefulness for SFT and test time scaling of Code LLM.
  arXiv  Detail & Related papers  (2025-09-15T08:38:01Z)
• ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large Language Models [81.12673534903979]
  Tool learning has emerged as a crucial capability for large language models (LLMs) to solve complex real-world tasks through interaction with external tools.<n>We propose ToolCoder, a novel framework that reformulates tool learning as a code generation task.
  arXiv  Detail & Related papers  (2025-02-17T03:42:28Z)
• Pseudocode-Injection Magic: Enabling LLMs to Tackle Graph Computational Tasks [15.69049038121735]
  Graph computational tasks are inherently challenging and often demand advanced algorithms for effective solutions.<n>Existing approaches are constrained by large language models' limited capability to comprehend complex graph structures.<n>We introduce a novel framework, PIE, which consists of three key steps: problem understanding, prompt design, and code generation.
  arXiv  Detail & Related papers  (2025-01-23T15:04:22Z)
• Interactive and Expressive Code-Augmented Planning with Large Language Models [62.799579304821826]
  Large Language Models (LLMs) demonstrate strong abilities in common-sense reasoning and interactive decision-making. Recent techniques have sought to structure LLM outputs using control flow and other code-adjacent techniques to improve planning performance. We propose REPL-Plan, an LLM planning approach that is fully code-expressive and dynamic.
  arXiv  Detail & Related papers  (2024-11-21T04:23:17Z)
• Subtle Errors in Reasoning: Preference Learning via Error-injected Self-editing [59.405145971637204]
  We propose a novel preference learning framework called eRror-Injected Self-Editing (RISE)<n>RISE injects predefined subtle errors into pivotal tokens in reasoning or steps to construct hard pairs for error mitigation.<n>Experiments validate the effectiveness of RISE, with preference learning on Qwen2-7B-Instruct yielding notable improvements of 3.0% on GSM8K and 7.9% on MATH with only 4.5K training samples.
  arXiv  Detail & Related papers  (2024-10-09T07:43:38Z)
• Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification [52.095460362197336]
  Large language models (LLMs) struggle with consistent and accurate reasoning. LLMs are trained primarily on correct solutions, reducing their ability to detect and learn from errors. We propose a novel collaborative method integrating Chain-of-Thought (CoT) and Program-of-Thought (PoT) solutions for verification.
  arXiv  Detail & Related papers  (2024-10-05T05:21:48Z)
• RGD: Multi-LLM Based Agent Debugger via Refinement and Generation Guidance [0.6062751776009752]
  Large Language Models (LLMs) have shown incredible potential in code generation tasks. LLMs can generate code based on task descriptions, but accuracy remains limited. We introduce a novel architecture of LLM-based agents for code generation and automatic debug: Refinement and Guidance debugger (RGD) RGD decomposes the code generation task into multiple steps, ensuring a clearer workflow and enabling iterative code refinement based on self-reflection and feedback.
  arXiv  Detail & Related papers  (2024-10-02T05:07:02Z)
• What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [80.18342600996601]
  Large language models (LLMs) produce code that is shorter yet more complicated as compared to canonical solutions. We develop a taxonomy of bugs for incorrect codes that includes three categories and 12 sub-categories, and analyze the root cause for common bug types. We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code based on bug types and compiler feedback.
  arXiv  Detail & Related papers  (2024-07-08T17:27:17Z)
• Validating LLM-Generated Programs with Metamorphic Prompt Testing [8.785973653167112]
  Large Language Models (LLMs) are increasingly integrated into the software development lifecycle. This paper proposes a novel solution called metamorphic prompt testing to address these challenges. Our evaluation on HumanEval shows that metamorphic prompt testing is able to detect 75 percent of the erroneous programs generated by GPT-4, with a false positive rate of 8.6 percent.
  arXiv  Detail & Related papers  (2024-06-11T00:40:17Z)
• Exploring Data-Efficient Adaptation of Large Language Models for Code Generation [64.5583894165813]
  We propose a novel adaptation approach named DEED, which stands for Data-Efficient adaptation with Error-Driven learning for code generation.<n> Experimental results show that, compared to other mainstream fine-tuning approaches, DEED achieves superior performance with few training data.
  arXiv  Detail & Related papers  (2024-02-29T16:09:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.