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RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair

If you use RepairLLaMA in academic research, please cite "RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair", Technical report, arXiv 2312.15698, 2023.

@techreport{repairllama2023,
  title={RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair},
  author={Silva, Andr{\'e} and Fang, Sen and Monperrus, Martin},
  url = {http://arxiv.org/abs/2312.15698},
  number = {2312.15698},
  institution = {arXiv},
}

This repository contains the code, model, and results to replicate the paper "RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair"

It is structured as follows:

  • repair-lora contains the RepairLLaMA low-rank adaptation of CodeLLaMA-7B, called "repair adapter"
  • results contains all generated patches for Defects4J and HumanEval-Java by all models (incl. full fine-tuning, lora, and code representations)
  • src contains the training and inference scripts
  • example contains an example notebook explaining how to load and prompt the RepairLLaMA model
  • build_ior contains scripts to generate datasets for different input-output representations (IOR).

Models

All fine-tuned models are available on HuggingFace, here are specific links:

Datasets

The processed fine-tuning datasets are made available on HuggingFace at https://huggingface.co/datasets/ASSERT-KTH/repairllama-datasets. It contains the datasets used for training the RepairLLaMA models, one subset per input/output representation pair. To get the 30k..50k datasets we did further filtering based on the token length of input + output pairs being less than 1024 tokens.

If it interests you, you can also find these on our HuggingFace org:

Benchmarks

The evaluation benchmarks are Defects4J v2 and HumanEval-Java.

We focus on single-function bugs (i.e. bugs whose developer patch exclusively changes one function):

Note that the original HumanEval-Java contains a duplicate bug.