Hybrid Automated Program Repair by Combining Large Language Models and Program Analysis

• URL: http://arxiv.org/abs/2406.00992v2
• Date: Tue, 4 Jun 2024 06:11:51 GMT
• Title: Hybrid Automated Program Repair by Combining Large Language Models and Program Analysis
• Authors: Fengjie Li, Jiajun Jiang, Jiajun Sun, Hongyu Zhang,
• Abstract summary: Automated Program Repair (APR) has garnered significant attention due to its potential to streamline the bug repair process for human developers. This paper introduces an innovative APR approach called GIANTREPAIR. Based on this insight, GIANTREPAIR first constructs patch skeletons from LLM-generated patches to confine the patch space, and then generates high-quality patches tailored to specific programs.
• Score: 12.7034916462208
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated Program Repair (APR) has garnered significant attention due to its potential to streamline the bug repair process for human developers. Recently, LLM-based APR methods have shown promise in repairing real-world bugs. However, existing APR methods often utilize patches generated by LLMs without further optimization, resulting in reduced effectiveness due to the lack of program-specific knowledge. Furthermore, the evaluations of these APR methods have typically been conducted under the assumption of perfect fault localization, which may not accurately reflect their real-world effectiveness. To address these limitations, this paper introduces an innovative APR approach called GIANTREPAIR. Our approach leverages the insight that LLM-generated patches, although not necessarily correct, offer valuable guidance for the patch generation process. Based on this insight, GIANTREPAIR first constructs patch skeletons from LLM-generated patches to confine the patch space, and then generates high-quality patches tailored to specific programs through context-aware patch generation by instantiating the skeletons. To evaluate the performance of our approach, we conduct two large-scale experiments. The results demonstrate that GIANTREPAIR not only effectively repairs more bugs (an average of 27.78% on Defects4J v1.2 and 23.40% on Defects4J v2.0) than using LLM-generated patches directly, but also outperforms state-of-the-art APR methods by repairing at least 42 and 7 more bugs under perfect and automated fault localization scenarios, respectively.

Related papers

• Do AI models help produce verified bug fixes? [62.985237003585674]
  Large Language Models are used to produce corrections to software bugs.<n>This paper investigates how programmers use Large Language Models to complement their own skills.<n>The results are a first step towards a proper role for AI and LLMs in providing guaranteed-correct fixes to program bugs.
  arXiv  Detail & Related papers  (2025-07-21T17:30:16Z)
• Accelerating Automatic Program Repair with Dual Retrieval-Augmented Fine-Tuning and Patch Generation on Large Language Models [28.75106676284909]
  We propose SelRepair, a novel APR approach with integration of a fine-tuned LLM with a newly-designed dual RAG module.<n>This approach uses a bug-fix pair dataset for fine-tuning and incorporates semantic and syntactic/structural similarity information through an RAG selection gate.<n> Evaluations on Java datasets show SelRepair outperforms other APR methods, achieving 26.29% and 17.64% in terms of exact match (EM) on different datasets while reducing inference time by at least 6.42% with controlled input lengths.
  arXiv  Detail & Related papers  (2025-07-14T09:41:51Z)
• Specification-Guided Repair of Arithmetic Errors in Dafny Programs using LLMs [84.30534714651093]
  We present an innovative APR tool for Dafny, a verification-aware programming language.<n>We localize faults through a series of steps, which include using Hoare Logic to determine the state of each statement within the program.<n>We evaluate our approach using DafnyBench, a benchmark of real-world Dafny programs.
  arXiv  Detail & Related papers  (2025-07-04T15:36:12Z)
• APRMCTS: Improving LLM-based Automated Program Repair with Iterative Tree Search [6.314858275160081]
  APRMCTS incorporates Monte Carlo Tree Search (MCTS) into patch searching by performing a global evaluation of the explored patches and selecting the most promising one for subsequent refinement and generation.<n>Our experiments on 835 bugs from Defects4J demonstrate that, when integrated with GPT-3.5, APRMCTS can fix a total of 201 bugs, which outperforms all state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-07-02T15:44:12Z)
• The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Models [48.073219761367184]
  We investigate an APR pipeline that balances the generation of multiple outputs and multiple rounds of iteration.<n>We fine-tune each model on an APR dataset with three sizes (1K, 30K, 65K) and two techniques (Full Fine-Tuning and LoRA)<n>Our results show that by using only a fraction (1%) of the fine-tuning dataset, we can achieve improvements of up to 78% in the number of plausible patches generated.
  arXiv  Detail & Related papers  (2025-05-05T18:06:51Z)
• Parameter-Efficient Fine-Tuning with Attributed Patch Semantic Graph for Automated Patch Correctness Assessment [8.028183762381474]
  Automated program repair (APR) aims to automatically repair program errors without human intervention.<n>Many research efforts have been devoted for automated patch correctness assessment ( APCA)
  arXiv  Detail & Related papers  (2025-05-05T13:15:53Z)
• Automated Refactoring of Non-Idiomatic Python Code: A Differentiated Replication with LLMs [54.309127753635366]
  We present the results of a replication study in which we investigate GPT-4 effectiveness in recommending and suggesting idiomatic actions. Our findings underscore the potential of LLMs to achieve tasks where, in the past, implementing recommenders based on complex code analyses was required.
  arXiv  Detail & Related papers  (2025-01-28T15:41:54Z)
• MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation Evaluators [53.91199933655421]
  Large Language Models (LLMs) have shown significant potential as judges for Machine Translation (MT) quality assessment. We introduce a universal and training-free framework, $textbfMQM-APE, to enhance the quality of error annotations predicted by LLM evaluators.
  arXiv  Detail & Related papers  (2024-09-22T06:43:40Z)
• A Case Study of LLM for Automated Vulnerability Repair: Assessing Impact of Reasoning and Patch Validation Feedback [7.742213291781287]
  We present VRpilot, a vulnerability repair technique based on reasoning and patch validation feedback. Our results show that VRpilot generates, on average, 14% and 7.6% more correct patches than the baseline techniques on C and Java.
  arXiv  Detail & Related papers  (2024-05-24T16:29:48Z)
• Revisiting Unnaturalness for Automated Program Repair in the Era of Large Language Models [9.454475517867817]
  We propose a patch-naturalness measurement, entropy-delta, to improve the efficiency of template-based repair techniques. Our proposed method can rank correct patches more effectively than state-of-the-art machine learning tools.
  arXiv  Detail & Related papers  (2024-04-23T17:12:45Z)
• Aligning LLMs for FL-free Program Repair [14.935596175148586]
  This paper investigates a new approach to adapt large language models (LLMs) to program repair. Our core insight is that LLM's APR capability can be greatly improved by simply aligning the output to their training objective. Based on this insight, we designed D4C, a straightforward prompting framework for APR.
  arXiv  Detail & Related papers  (2024-04-13T02:36:40Z)
• Are Large Language Models Good Prompt Optimizers? [65.48910201816223]
  We conduct a study to uncover the actual mechanism of LLM-based Prompt Optimization. Our findings reveal that the LLMs struggle to identify the true causes of errors during reflection, tending to be biased by their own prior knowledge. We introduce a new "Automatic Behavior Optimization" paradigm, which directly optimize the target model's behavior in a more controllable manner.
  arXiv  Detail & Related papers  (2024-02-03T09:48:54Z)
• A Novel Approach for Automatic Program Repair using Round-Trip Translation with Large Language Models [50.86686630756207]
  Research shows that grammatical mistakes in a sentence can be corrected by translating it to another language and back. Current generative models for Automatic Program Repair (APR) are pre-trained on source code and fine-tuned for repair. This paper proposes bypassing the fine-tuning step and using Round-Trip Translation (RTT): translation of code from one programming language to another programming or natural language, and back.
  arXiv  Detail & Related papers  (2024-01-15T22:36:31Z)
• Enhancing Redundancy-based Automated Program Repair by Fine-grained Pattern Mining [18.3896381051331]
  We propose a new repair technique named Repatt, which incorporates a two-level pattern mining process for guiding effective patch generation. We have conducted an experiment on the widely-used Defects4J benchmark and compared Repatt with eight state-of-the-art APR approaches.
  arXiv  Detail & Related papers  (2023-12-26T08:42:32Z)
• RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair [75.40584530380589]
  We propose a novel Retrieval-Augmented Patch Generation framework (RAP-Gen) RAP-Gen explicitly leveraging relevant fix patterns retrieved from a list of previous bug-fix pairs. We evaluate RAP-Gen on three benchmarks in two programming languages, including the TFix benchmark in JavaScript, and Code Refinement and Defects4J benchmarks in Java.
  arXiv  Detail & Related papers  (2023-09-12T08:52:56Z)
• Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies [104.32199881187607]
  Large language models (LLMs) have demonstrated remarkable performance across a wide array of NLP tasks. A promising approach to rectify these flaws is self-correction, where the LLM itself is prompted or guided to fix problems in its own output. This paper presents a comprehensive review of this emerging class of techniques.
  arXiv  Detail & Related papers  (2023-08-06T18:38:52Z)
• Revisiting the Plastic Surgery Hypothesis via Large Language Models [9.904030364454563]
  We propose FitRepair, which combines the direct usage of Large Language Models with two domain-specific fine-tuning strategies and one prompting strategy for more powerful APR. Our experiments on the widely studied Defects4j 1.2 and 2.0 datasets show that FitRepair fixes 89 and 44 bugs.
  arXiv  Detail & Related papers  (2023-03-18T20:33:46Z)
• Practical Program Repair in the Era of Large Pre-trained Language Models [13.694803023685175]
  Automated Program Repair (APR) aims to help developers automatically patch software bugs. PLMs, trained using billions of text/code tokens, can potentially help avoid this issue. We select 9 recent state-of-the-art PLMs, including both generative and infilling models, ranging from 125M to 20B in size.
  arXiv  Detail & Related papers  (2022-10-25T17:18:02Z)
• Optimizing Two-way Partial AUC with an End-to-end Framework [154.47590401735323]
  Area Under the ROC Curve (AUC) is a crucial metric for machine learning. Recent work shows that the TPAUC is essentially inconsistent with the existing Partial AUC metrics. We present the first trial in this paper to optimize this new metric.
  arXiv  Detail & Related papers  (2022-06-23T12:21:30Z)

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.