Code Repair

7 papers with code • 1 benchmarks • 6 datasets

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Most implemented papers

CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

microsoft/CodeXGLUE 9 Feb 2021

Benchmark datasets have a significant impact on accelerating research in programming language tasks.

Learning Performance-Improving Code Edits

madaan/pie-perf 15 Feb 2023

Next, we propose a broad range of adaptation strategies for code optimization; for prompting, these include retrieval-based few-shot prompting and chain-of-thought, and for finetuning, these include performance-conditioned generation and synthetic data augmentation based on self-play.

OctoPack: Instruction Tuning Code Large Language Models

bigcode-project/bigcode-evaluation-harness 14 Aug 2023

We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46. 2% pass@1).

MACER: A Modular Framework for Accelerated Compilation Error Repair

purushottamkar/macer 28 May 2020

Automated compilation error repair, the problem of suggesting fixes to buggy programs that fail to compile, has generated significant interest in recent years.

Break-It-Fix-It: Unsupervised Learning for Program Repair

michiyasunaga/bifi 11 Jun 2021

To bridge this gap, we propose a new training approach, Break-It-Fix-It (BIFI), which has two key ideas: (i) we use the critic to check a fixer's output on real bad inputs and add good (fixed) outputs to the training data, and (ii) we train a breaker to generate realistic bad code from good code.

Guiding Language Models of Code with Global Context using Monitors

microsoft/monitors4codegen 19 Jun 2023

We construct a repository-level dataset PragmaticCode for method-completion in Java and evaluate MGD on it.

INTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repair

neuir/intervenor 16 Nov 2023

INTERVENOR prompts Large Language Models (LLMs) to play distinct roles during the code repair process, functioning as both a Code Learner and a Code Teacher.