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EXPERIMENT.md

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Replicating results on SWE-bench-lite

Setup

Docker

Important

There can be minor improvements to the Docker image from time to time. Please pull the latest version of the image.

Note

The experiments were conducted on Ubuntu 20.04. Since SWE-bench evaluation may have different behavior under different host systems, it is recommened to run the provided docker on Ubuntu 20.04.

We have built a docker image with all task instances environment in it (it's a large image ~25GB!). With this image, you can directly start an experiment run.

docker pull yuntongzhang/auto-code-rover:experiment

Experiment preparation

Start a container:

docker run -it yuntongzhang/auto-code-rover:experiment

Activate the conda environment in it.

conda activate auto-code-rover

Set some temp git info:

git config --global user.email [email protected]
git config --global user.name acr

In the container, specify your OpenAI key in the OPENAI_KEY environment variable:

export OPENAI_KEY=xx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

Run!

In the /opt/auto-code-rover/ directory in the container, issue the following command to start the experiment run using gpt-4-0125-preview on SWE-bench-lite. (From our experience, one run with gpt-4-0125-preview on the 300 instances costs <150 USD on OpenAI API.)

python scripts/run.py conf/vanilla-lite.conf

This command runs auto-code-rover on the 300 instances in SWE-bench-lite, and runs the SWE-bench evaluation on the generated patches. The final results of the experiment will be at /opt/auto-code-rover/experiment/vanilla-lite/final_report.json.

Running experiments multiple times

When running multiple experiments (sequentially), we recommend using a different id in the conf file for each experiment. This is because the outputs for an experiment will be created in a directory using id as the name.

If you want to use the same id for multiple experiments and overwrite the previous experiment results, you can use the -f option of scripts/run.py. This will remove the previous experiment results with the same id.

Note on running experiments in parallel

We do not recommend creating multiple processes running the script scripts/run.py. This is because different tasks instances may share the same copy of local code base (e.g. astropy-6938 and astropy-7746 share the same codebase at setup_astropy__astropy__1.3).

Instead, we support parallelism of experiments in scripts/run.py itself. Please set the value of num_processes in the conf file to control how many tasks can be run in parallel. The scripts internally handle the parallelism issue mentioned above.

Changing the conf file

You can modify the conf/vanilla-lite.conf file to set parameters such as model temperature etc. Here are a few useful fields in the conf file:

  • id: determines the name of the experiment output folder

  • experiment_dir: where output will be stored

  • setup_result_dir: must point to the directory where SWE-bench setup writes its results

  • eval_log_dir: where the SWE-bench evaluation log is written to

  • model: the model to be used by auto-code-rover

  • temperature: model temperature

  • conv_round_limit: rounds limit for the conversation with context retrieval agent

  • selected_tasks_file: a file containing ids of all tasks to be run

  • print: whether to the print more info to console

  • num_processes: number of parallel processes when running auto-code-rover. Should not be too large, otherwise parallelly running multiple task instances can exceed OpenAI token limit and cause the task instance to fail.

Contacts

Note

If you encounter any issue in the replication experiment, you can open an GitHub issue or contact us at {yuntong,hruan,zhiyufan}@comp.nus.edu.sg.