APPT: Boosting Automated Patch Correctness Prediction via Fine-tuning Pre-trained Models

• URL: http://arxiv.org/abs/2301.12453v2
• Date: Tue, 16 Jan 2024 03:41:53 GMT
• Title: APPT: Boosting Automated Patch Correctness Prediction via Fine-tuning Pre-trained Models
• Authors: Quanjun Zhang, Chunrong Fang, Weisong Sun, Yan Liu, Tieke He, Xiaodong Hao, Zhenyu Chen
• Abstract summary: We propose APPT, a pre-trained model-based automated patch correctness assessment technique by both pre-training and fine-tuning. We conduct an experiment on 1,183 Defects4J patches and the experimental results show that APPT achieves prediction accuracy of 79.7% and recall of 83.2%.
• Score: 15.179895484968476
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated program repair (APR) aims to fix software bugs automatically without human debugging efforts and plays a crucial role in software development and maintenance. Despite promising, APR is still challenged by a long-standing overfitting problem (i.e., the generated patch is plausible but overfitting). Various techniques have thus been proposed to address the overfitting problem. Recently, researchers have employed BERT to extract code features, which are then used to train a classifier for patch correctness prediction. However, BERT is restricted to feature extraction for classifier training without benefiting from the training process, potentially generating sub-optimal vector representations for patched code snippets. In this paper, we propose APPT, a pre-trained model-based automated patch correctness assessment technique by both pre-training and fine-tuning. APPT adopts a pre-trained model as the encoder stack, followed by an LSTM stack and a deep learning classifier. More importantly, the pre-trained model is fine-tuned in conjunction with other components as a whole pipeline to fully adapt it specifically for reasoning about patch correctness. We conduct an extensive experiment on 1,183 Defects4J patches and the experimental results show that APPT achieves prediction accuracy of 79.7% and recall of 83.2%, outperforming CACHE by 4.3% and 6.7%. Our additional investigation on 49,694 real-world patches shows that APPT achieves the optimum performance compared with existing representation learning techniques. We further investigate the impact of each component and find that they all positively contribute to APPT, e.g., the fine-tuning process and the LSTM stack increase F1-score by 10.22% and 4.11%, respectively. We also prove that adopting advanced pre-trained models can further provide substantial advancement, highlighting the generalizability of APPT.

Related papers

• 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)
• Revisiting the Power of Prompt for Visual Tuning [50.11465784194896]
  This study explores the correlation evolvement between prompts and patch tokens during proficient training. Inspired by the observation that the prompt tokens tend to share high mutual information with patch tokens, we propose initializing prompts with downstream token prototypes. Our method significantly advances the adaptation for self-supervised pretraining, achieving impressive task performance gains of at least 10% to 30%.
  arXiv  Detail & Related papers  (2024-02-04T07:49:02Z)
• 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)
• Approximated Prompt Tuning for Vision-Language Pre-trained Models [54.326232586461614]
  In vision-language pre-trained models, prompt tuning often requires a large number of learnable tokens to bridge the gap between the pre-training and downstream tasks. We propose a novel Approximated Prompt Tuning (APT) approach towards efficient VL transfer learning.
  arXiv  Detail & Related papers  (2023-06-27T05:43:47Z)
• An Empirical Analysis of Parameter-Efficient Methods for Debiasing Pre-Trained Language Models [55.14405248920852]
  We conduct experiments with prefix tuning, prompt tuning, and adapter tuning on different language models and bias types to evaluate their debiasing performance. We find that the parameter-efficient methods are effective in mitigating gender bias, where adapter tuning is consistently the most effective. We also find that prompt tuning is more suitable for GPT-2 than BERT, and racial and religious bias is less effective when it comes to racial and religious bias.
  arXiv  Detail & Related papers  (2023-06-06T23:56:18Z)
• PatchZero: Zero-Shot Automatic Patch Correctness Assessment [13.19425284402493]
  We propose toolname, the patch correctness assessment by adopting a large language model for code. toolname prioritizes labeled patches from existing APR tools that exhibit semantic similarity to those generated by new APR tools. Our experimental results showed that toolname can achieve an accuracy of 84.4% and an F1-score of 86.5% on average.
  arXiv  Detail & Related papers  (2023-03-01T03:12:11Z)
• Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning [6.269370220586248]
  In this paper, we propose a novel technique to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. We have conducted experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline.
  arXiv  Detail & Related papers  (2023-01-03T14:16:32Z)
• Improving Automated Program Repair with Domain Adaptation [0.0]
  Automated Program Repair (APR) is defined as the process of fixing a bug/defect in the source code, by an automated tool. APR tools have recently experienced promising results by leveraging state-of-the-art Neural Language Processing (NLP) techniques.
  arXiv  Detail & Related papers  (2022-12-21T23:52:09Z)
• Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models [107.05966685291067]
  We propose test-time prompt tuning (TPT) to learn adaptive prompts on the fly with a single test sample. TPT improves the zero-shot top-1 accuracy of CLIP by 3.6% on average. In evaluating cross-dataset generalization with unseen categories, TPT performs on par with the state-of-the-art approaches that use additional training data.
  arXiv  Detail & Related papers  (2022-09-15T17:55:11Z)
• Sample-Efficient Optimisation with Probabilistic Transformer Surrogates [66.98962321504085]
  This paper investigates the feasibility of employing state-of-the-art probabilistic transformers in Bayesian optimisation. We observe two drawbacks stemming from their training procedure and loss definition, hindering their direct deployment as proxies in black-box optimisation. We introduce two components: 1) a BO-tailored training prior supporting non-uniformly distributed points, and 2) a novel approximate posterior regulariser trading-off accuracy and input sensitivity to filter favourable stationary points for improved predictive performance.
  arXiv  Detail & Related papers  (2022-05-27T11:13:17Z)
• Checking Patch Behaviour against Test Specification [4.723400023753107]
  We propose a hypothesis on how the link between the patch behaviour and failing test specifications can be drawn. We then propose BATS, an unsupervised learning-based system to predict patch correctness.
  arXiv  Detail & Related papers  (2021-07-28T11:39:06Z)

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.