LLMs as Continuous Learners: Improving the Reproduction of Defective Code in Software Issues

• URL: http://arxiv.org/abs/2411.13941v1
• Date: Thu, 21 Nov 2024 08:49:23 GMT
• Title: LLMs as Continuous Learners: Improving the Reproduction of Defective Code in Software Issues
• Authors: Yalan Lin, Yingwei Ma, Rongyu Cao, Binhua Li, Fei Huang, Xiaodong Gu, Yongbin Li,
• Abstract summary: EvoCoder is a continuous learning framework for issue code reproduction. Our results show a 20% improvement in issue reproduction rates over existing SOTA methods.
• Score: 62.12404317786005
• License:
• Abstract: Reproducing buggy code is the first and crucially important step in issue resolving, as it aids in identifying the underlying problems and validating that generated patches resolve the problem. While numerous approaches have been proposed for this task, they primarily address common, widespread errors and struggle to adapt to unique, evolving errors specific to individual code repositories. To fill this gap, we propose EvoCoder, a multi-agent continuous learning framework for issue code reproduction. EvoCoder adopts a reflection mechanism that allows the LLM to continuously learn from previously resolved problems and dynamically refine its strategies to new emerging challenges. To prevent experience bloating, EvoCoder introduces a novel hierarchical experience pool that enables the model to adaptively update common and repo-specific experiences. Our experimental results show a 20\% improvement in issue reproduction rates over existing SOTA methods. Furthermore, integrating our reproduction mechanism significantly boosts the overall accuracy of the existing issue-resolving pipeline.

Related papers

• Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review [50.67937325077047]
  This paper is devoted to a comprehensive review of realizing the sample efficiency and generalization of RL algorithms through transfer and inverse reinforcement learning (T-IRL) Our findings denote that a majority of recent research works have dealt with the aforementioned challenges by utilizing human-in-the-loop and sim-to-real strategies. Under the IRL structure, training schemes that require a low number of experience transitions and extension of such frameworks to multi-agent and multi-intention problems have been the priority of researchers in recent years.
  arXiv  Detail & Related papers  (2024-11-15T15:18:57Z)
• A Comprehensive Survey of AI-Driven Advancements and Techniques in Automated Program Repair and Code Generation [0.0]
  27 recent papers have been reviewed and split into two groups. The first group consists of new methods for bug detection and repair, which include locating semantic errors. The second group dwells on code generation, providing an overview of both general-purpose LLMs fine-tuned for programming and task-specific models. It also presents methods to improve code generation, such as identifier-aware training, fine-tuning at the instruction level, and incorporating semantic code structures.
  arXiv  Detail & Related papers  (2024-11-12T06:47:54Z)
• An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking [50.81324768683995]
  FIRST is a novel approach that integrates a learning-to-rank objective and leveraging the logits of only the first generated token. We extend the evaluation of FIRST to the TREC Deep Learning datasets (DL19-22), validating its robustness across diverse domains. Our experiments confirm that fast reranking with single-token logits does not compromise out-of-domain reranking quality.
  arXiv  Detail & Related papers  (2024-11-08T12:08:17Z)
• 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)
• An Empirical Study on Self-correcting Large Language Models for Data Science Code Generation [1.335664823620186]
  Large Language Models (LLMs) have recently advanced many applications on software engineering tasks. CoT-SelfEvolve iteratively and automatically refines code through a self-correcting process.
  arXiv  Detail & Related papers  (2024-08-28T09:19:09Z)
• 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)
• Agent-Driven Automatic Software Improvement [55.2480439325792]
  This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs) The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation. We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
  arXiv  Detail & Related papers  (2024-06-24T15:45:22Z)
• 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)
• CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning [92.36705236706678]
  "CodeRL" is a new framework for program synthesis tasks through pretrained LMs and deep reinforcement learning. During inference, we introduce a new generation procedure with a critical sampling strategy. For the model backbones, we extended the encoder-decoder architecture of CodeT5 with enhanced learning objectives.
  arXiv  Detail & Related papers  (2022-07-05T02:42:15Z)

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.