Invariant-based Program Repair

• URL: http://arxiv.org/abs/2312.16652v2
• Date: Fri, 26 Jan 2024 20:20:20 GMT
• Title: Invariant-based Program Repair
• Authors: Omar I. Al-Bataineh
• Abstract summary: This paper describes a formal general-purpose automated program repair (APR) framework based on the concept of program invariants. We demonstrate the usefulness of leveraging invariants in APR by developing an invariant-based repair system for performance bugs.
• Score: 1.7767466724342067
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper describes a formal general-purpose automated program repair (APR) framework based on the concept of program invariants. In the presented repair framework, the execution traces of a defected program are dynamically analyzed to infer specifications $\varphi_{correct}$ and $\varphi_{violated}$, where $\varphi_{correct}$ represents the set of likely invariants (good patterns) required for a run to be successful and $\varphi_{violated}$ represents the set of likely suspicious invariants (bad patterns) that result in the bug in the defected program. These specifications are then refined using rigorous program analysis techniques, which are also used to drive the repair process towards feasible patches and assess the correctness of generated patches.We demonstrate the usefulness of leveraging invariants in APR by developing an invariant-based repair system for performance bugs. The initial analysis shows the effectiveness of invariant-based APR in handling performance bugs by producing patches that ensure program's efficiency increase without adversely impacting its functionality.

Related papers

• 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)
• Hybrid Automated Program Repair by Combining Large Language Models and Program Analysis [12.7034916462208]
  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.
  arXiv  Detail & Related papers  (2024-06-03T05:05:12Z)
• Sparse is Enough in Fine-tuning Pre-trained Large Language Models [98.46493578509039]
  We propose a gradient-based sparse fine-tuning algorithm, named Sparse Increment Fine-Tuning (SIFT) We validate its effectiveness on a range of tasks including the GLUE Benchmark and Instruction-tuning.
  arXiv  Detail & Related papers  (2023-12-19T06:06:30Z)
• Diagnosing and Rectifying Fake OOD Invariance: A Restructured Causal Approach [51.012396632595554]
  Invariant representation learning (IRL) encourages the prediction from invariant causal features to labels de-confounded from the environments. Recent theoretical results verified that some causal features recovered by IRLs merely pretend domain-invariantly in the training environments but fail in unseen domains. We develop an approach based on conditional mutual information with respect to RS-SCM, then rigorously rectify the spurious and fake invariant effects.
  arXiv  Detail & Related papers  (2023-12-15T12:58:05Z)
• 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)
• CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks [58.29502185344086]
  In safety-critical machine learning applications, it is crucial to defend models against adversarial attacks. It is important to provide provable guarantees for deep learning models against semantically meaningful input transformations. We propose a new universal probabilistic certification approach based on Chernoff-Cramer bounds.
  arXiv  Detail & Related papers  (2021-09-22T12:46:04Z)
• Towards a Theoretical Understanding of the Robustness of Variational Autoencoders [82.68133908421792]
  We make inroads into understanding the robustness of Variational Autoencoders (VAEs) to adversarial attacks and other input perturbations. We develop a novel criterion for robustness in probabilistic models: $r$-robustness. We show that VAEs trained using disentangling methods score well under our robustness metrics.
  arXiv  Detail & Related papers  (2020-07-14T21:22:29Z)
• Target-Embedding Autoencoders for Supervised Representation Learning [111.07204912245841]
  This paper analyzes a framework for improving generalization in a purely supervised setting, where the target space is high-dimensional. We motivate and formalize the general framework of target-embedding autoencoders (TEA) for supervised prediction, learning intermediate latent representations jointly optimized to be both predictable from features as well as predictive of targets.
  arXiv  Detail & Related papers  (2020-01-23T02:37:10Z)

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.