The Impact of Program Reduction on Automated Program Repair

• URL: http://arxiv.org/abs/2408.01134v1
• Date: Fri, 2 Aug 2024 09:23:45 GMT
• Title: The Impact of Program Reduction on Automated Program Repair
• Authors: Linas Vidziunas, David Binkley, Leon Moonen,
• Abstract summary: We describe a program repair approach that aims to improve the scalability of modern APR tools. We investigate slicing's impact on all three phases of the repair process: fault localization, patch generation, and patch validation. We conclude that program reduction can improve the performance of APR without degrading repair quality, but this improvement is not universal.
• Score: 0.3277163122167433
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Correcting bugs using modern Automated Program Repair (APR) can be both time-consuming and resource-expensive. We describe a program repair approach that aims to improve the scalability of modern APR tools. The approach leverages program reduction in the form of program slicing to eliminate code irrelevant to fixing the bug, which improves the APR tool's overall performance. We investigate slicing's impact on all three phases of the repair process: fault localization, patch generation, and patch validation. Our empirical exploration finds that the proposed approach, on average, enhances the repair ability of the TBar APR tool, but we also discovered a few cases where it was less successful. Specifically, on examples from the widely used Defects4J dataset, we obtain a substantial reduction in median repair time, which falls from 80 minutes to just under 18 minutes. We conclude that program reduction can improve the performance of APR without degrading repair quality, but this improvement is not universal. A replication package is available via Zenodo at https://doi.org/10.5281/zenodo.13074333. Keywords: automated program repair, dynamic program slicing, fault localization, test-suite reduction, hybrid techniques.

Related papers

• EoRA: Training-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation [79.56709262189953]
  EoRA consistently outperforms previous methods in compensating errors for compressed LLaMA2/3 models on various tasks. EoRA offers a scalable, training-free solution to compensate for compression errors.
  arXiv  Detail & Related papers  (2024-10-28T17:59:03Z)
• FastFixer: An Efficient and Effective Approach for Repairing Programming Assignments [21.848112758958543]
  We propose FastFixer, an efficient and effective approach for programming assignment repair. We first propose a novel repair-oriented fine-tuning strategy, aiming to enhance the LLM's attention towards learning how to generate the necessary patch and its associated context. Considering the repair efficiency, FastFixer achieves a remarkable inference speedup of 16.67 times compared to the autoregressive decoding algorithm.
  arXiv  Detail & Related papers  (2024-10-11T10:17:02Z)
• Towards Practical and Useful Automated Program Repair for Debugging [4.216808129651161]
  PracAPR is an interactive repair system that works in an Integrated Development Environment (IDE) PracAPR does not require a test suite or program re-execution.
  arXiv  Detail & Related papers  (2024-07-12T03:19:54Z)
• On The Effectiveness of Dynamic Reduction Techniques in Automated Program Repair [1.7767466724342067]
  We describe a program repair framework that effectively handles large-scale buggy programs of industrial complexity. The framework exploits program reduction in the form of program slicing to eliminate parts of the code irrelevant to the bug being repaired. Our empirical results on the widely used Defects4J dataset reveal that a substantial improvement in performance can be obtained without any degradation in repair quality.
  arXiv  Detail & Related papers  (2024-06-23T21:35:07Z)
• Investigating the Transferability of Code Repair for Low-Resource Programming Languages [57.62712191540067]
  Large language models (LLMs) have shown remarkable performance on code generation tasks. Recent works augment the code repair process by integrating modern techniques such as chain-of-thought reasoning or distillation. We investigate the benefits of distilling code repair for both high and low resource languages.
  arXiv  Detail & Related papers  (2024-06-21T05:05:39Z)
• 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)
• Practical Program Repair via Preference-based Ensemble Strategy [28.176710503313895]
  We propose a Preference-based Ensemble Program Repair framework (P-EPR) to rank APR tools for repairing different bugs. P-EPR is the first non-learning-based APR ensemble method that is novel in its exploitation of repair patterns. Experimental results show that P-EPR outperforms existing strategies significantly both in flexibility and effectiveness.
  arXiv  Detail & Related papers  (2023-09-15T07:23:04Z)
• 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)
• Learning from Self-Sampled Correct and Partially-Correct Programs [96.66452896657991]
  We propose to let the model perform sampling during training and learn from both self-sampled fully-correct programs and partially-correct programs. We show that our use of self-sampled correct and partially-correct programs can benefit learning and help guide the sampling process. Our proposed method improves the pass@k performance by 3.1% to 12.3% compared to learning from a single reference program with MLE.
  arXiv  Detail & Related papers  (2022-05-28T03:31:07Z)
• Graph-based, Self-Supervised Program Repair from Diagnostic Feedback [108.48853808418725]
  We introduce a program-feedback graph, which connects symbols relevant to program repair in source code and diagnostic feedback. We then apply a graph neural network on top to model the reasoning process. We present a self-supervised learning paradigm for program repair that leverages unlabeled programs available online.
  arXiv  Detail & Related papers  (2020-05-20T07:24:28Z)

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.