ThinkRepair: Self-Directed Automated Program Repair

This repo contains both the correct patches generated by our study along with the code used to run the experiment.

Prerequisites

Install Defects4J from https://github.com/rjust/defects4j 
export PATH=$PATH:"path2defects4j"/framework/bin

JDK 1.8 for Defects4J
export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-amd64
export PATH=$JAVA_HOME/bin:$PATH
export CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar

Download RWB datasets from https://figshare.com/s/b8fa6587bfcb9908b0da

Plausible patches generation

Example usage to run ThinkRepair_ChatGPT: select: Selection strategy to use, current support: CSelect, SSelect, RSelect. pfl: Use the perfect fault information. n_example: Number of examples. sample: The maximum number of repair attempts. chance: The maximum number of interactions. api_key: Place your ChatGPT access key.

python repair_chatgpt.py --dataset D4JV1.2 \
               --select CSelect \
               --pfl True \
               --n_example 2 \
               --sample 25 \
               --chance 5

Example usage to run ThinkRepair_open_source_llm: STARCODER_AUTH_TOKEN: Place your starcoder auto token.

python repair_open_source.py --dataset D4JV1.2 \
               --select CSelect \
               --pfl True \
               --n_example 2 \
               --sample 25 \
               --chance 5 \

After running ThinkRepair, your directory structure should be like the following:

ThinkRepair
├── Datasets
    ├── D4J
        ├── generate_prompt*: input for the collection phase
        ├── repair_prompt*: input for the fixing phase
    ├── ...
├── Results
    ├── ChatGPT
        ├── D4JV1.2
            ├── cot_pfl.json
            ├── repair_pfl.json
        ├── ...
    ├── CodeLlama
        ├── D4JV1.2
            ├── cot_pfl.json
            ├── repair_pfl.json
        ├── ...
    ├── ...
├── ...