157 citations · 512 across the 11 of their papers we have counts for
11 papers
ExecVerify: White-Box RL with Verifiable Stepwise Rewards for Code Execution Reasoning
Lingxiao Tang, He Ye, Zhaoyang Chu +4
Code LLMs still struggle with code execution reasoning, especially in smaller models. Existing methods rely on supervised fine-tuning (SFT) with teacher-generated explanations, pri…
Prometheus: Towards Long-Horizon Codebase Navigation for Repository-Level Problem Solving
Yue Pan, Zimin Chen, Siyu Lu +8
Large Language Models (LLMs) have shown remarkable capabilities in automating software engineering tasks, spurring the emergence of coding agents that scaffold LLMs with external t…
MUFIN: Improving Neural Repair Models with Back-Translation
André Silva, João F. Ferreira, He Ye +1
Automated program repair is the task of automatically repairing software bugs. A promising direction in this field is self-supervised learning, a learning paradigm in which repair…
ITER: Iterative Neural Repair for Multi-Location Patches
He Ye, Martin Monperrus
Automated program repair (APR) has achieved promising results, especially using neural networks. Yet, the overwhelming majority of patches produced by APR tools are confined to one…
SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics
He Ye, Matias Martinez, Xiapu Luo +2
Learning-based program repair has achieved good results in a recent series of papers. Yet, we observe that the related work fails to repair some bugs because of a lack of knowledge…
Megadiff: A Dataset of 600k Java Source Code Changes Categorized by Diff Size
Martin Monperrus, Matias Martinez, He Ye +3
This paper presents Megadiff, a dataset of source code diffs. It focuses on Java, with strict inclusion criteria based on commit message and diff size. Megadiff contains 663 029 Ja…
Neural Program Repair with Execution-based Backpropagation
He Ye, Matias Martinez, Martin Monperrus
Neural machine translation (NMT) architectures have achieved promising results for automatic program repair. Yet, they have the limitation of generating low-quality patches (e.g.,…
A Software-Repair Robot based on Continual Learning
Benoit Baudry, Zimin Chen, Khashayar Etemadi +8
Software bugs are common and correcting them accounts for a significant part of costs in the software development and maintenance process. This calls for automatic techniques to de…
Automated Classification of Overfitting Patches with Statically Extracted Code Features
He Ye, Jian Gu, Matias Martinez +2
Automatic program repair (APR) aims to reduce the cost of manually fixing software defects. However, APR suffers from generating a multitude of overfitting patches, those patches t…
Automated Patch Assessment for Program Repair at Scale
He Ye, Matias Martinez, Martin Monperrus
In this paper, we do automatic correctness assessment for patches generated by program repair systems. We consider the human-written patch as ground truth oracle and randomly gener…
A Comprehensive Study of Automatic Program Repair on the QuixBugs Benchmark
He Ye, Matias Martinez, Thomas Durieux +1
Automatic program repair papers tend to repeatedly use the same benchmarks. This poses a threat to the external validity of the findings of the program repair research community. I…