OpenHands/evaluation/swe_bench

SWE-Bench Evaluation with OpenDevin SWE-Bench Docker Image

This folder contains the evaluation harness that we built on top of the original SWE-Bench benchmark (paper). We created a fork of SWE-Bench mostly built on top of the original repo and containerized it for easy evaluation.

Setup Environment

Please follow this document to set up a local development environment for OpenDevin.

OpenDevin SWE-Bench Docker Image

In OpenDevin-SWE-Bench fork (mostly from original repo with some fixes), we try to pre-build the testbed (i.e., code of the repository we want the agent to edit) AND the conda environment, so that in evaluation (inference) time, we can directly leverage existing environments for efficient evaluation.

We pack everything you need for SWE-Bench evaluation into one, gigantic, docker image. To use it:

docker pull ghcr.io/opendevin/eval-swe-bench:full-v1.2.1

The Docker image contains several important directories:

• /swe_util/OD-SWE-bench: root directory for the OD-SWE-bench repository
• /swe_util/eval_data: directory to eval data
  • /swe_util/eval_data/eval_logs/: evaluation logs
  • /swe_util/eval_data/eval_temp/: temporary folder for the evaluation process
  • /swe_util/eval_data/instances/: swe-bench raw instances
  • /swe_util/eval_data/outputs/: model or agent outputs
  • /swe_util/eval_data/testbed_logs/: logs for testbed building
  • /swe_util/eval_data/testbeds/: directory for all testbeds
• /swe_util/miniforge3/: directory for miniforge3

To reproduce how we pack the image, check this doc.

NOTE: We only support SWE-Bench lite for now. But modifying our existing scripts for full SWE-Bench should be quite straightforward.

Configure OpenDevin and your LLM

Create a config.toml file if it does not exist at the root of the workspace.

Add the following configurations:

[core]
max_iterations = 100
cache_dir = "/tmp/cache"
sandbox_container_image = "ghcr.io/opendevin/sandbox:latest"
sandbox_type = "ssh"
ssh_hostname = "localhost"
sandbox_timeout = 120

# SWEBench eval specific
use_host_network = false
run_as_devin = false
enable_auto_lint = true

# TODO: Change these to the model you want to evaluate
[eval_gpt4_1106_preview]
model = "gpt-4-1106-preview"
api_key = "XXX"
temperature = 0.0

[eval_some_openai_compatible_model]
model = "openai/MODEL_NAME"
base_url = "https://OPENAI_COMPATIBLE_URL/v1"
api_key = "XXX"
temperature = 0.0

Test if your environment works

Make sure your Docker daemon is running, and you have pulled the eval-swe-bench:full-v1.2 docker image. Then run this python script:

poetry run python evaluation/swe_bench/swe_env_box.py

If you get to the interactive shell successfully, it means your environment works! If you see an error, please make sure your config.toml contains all SWEBench eval specific settings as shown in the previous section.

Run Inference on SWE-Bench Instances

./evaluation/swe_bench/scripts/run_infer.sh [model_config] [agent] [eval_limit]
# e.g., ./evaluation/swe_bench/scripts/run_infer.sh eval_gpt4_1106_preview CodeActAgent 300

where model_config is mandatory, while agent and eval_limit are optional.

model_config, e.g. eval_gpt4_1106_preview, is the config group name for your LLM settings, as defined in your config.toml.

agent, e.g. CodeActAgent, is the name of the agent for benchmarks, defaulting to CodeActAgent.

eval_limit, e.g. 10, limits the evaluation to the first eval_limit instances. By default, the script evaluates the entire SWE-bench_Lite test set (300 issues). Note: in order to use eval_limit, you must also set agent.

Let's say you'd like to run 10 instances using eval_gpt4_1106_preview and CodeActAgent, then your command would be:

./evaluation/swe_bench/scripts/run_infer.sh eval_gpt4_1106_preview CodeActAgent 10

If you would like to specify a list of tasks you'd like to benchmark on, you could create a config.toml under ./evaluation/swe_bench/ folder, and put a list attribute named selected_ids, e.g.

selected_ids = ['sphinx-doc__sphinx-8721', 'sympy__sympy-14774', 'scikit-learn__scikit-learn-10508']

Then only these tasks (rows whose instance_id is in the above list) will be evaluated. In this case, eval_limit option applies to tasks that are in the selected_ids list.

After running the inference, you will obtain a output.jsonl (by default it will be saved to evaluation/evaluation_outputs).

Evaluate Generated Patches

With output.jsonl file, you can run eval_infer.sh to evaluate generated patches, and produce a fine-grained report.

If you want to evaluate existing results, you should first run this to clone existing outputs

git clone https://huggingface.co/spaces/OpenDevin/evaluation evaluation/evaluation_outputs

Then you can run the following:

# ./evaluation/swe_bench/scripts/eval_infer.sh $YOUR_OUTPUT_JSONL
# For example:
./evaluation/swe_bench/scripts/eval_infer.sh evaluation/evaluation_outputs/outputs/swe_bench/CodeActAgent/gpt-4-1106-preview_maxiter_50_N_v1.0/output.jsonl

The final results will be saved to evaluation/evaluation_outputs/outputs/swe_bench/CodeActAgent/gpt-4-1106-preview_maxiter_50_N_v1.0/output.merged.jsonl.

It will contain an additional field fine_grained_report (see example below) compared to the output.jsonl from the previous inference stage.

"fine_grained_report": {
  "gold_tests": {
    "FAIL_TO_PASS": "[\"tests/test_ext_viewcode.py::test_viewcode_epub_default\"]",
    "PASS_TO_PASS": "[\"tests/test_ext_viewcode.py::test_viewcode_epub_enabled\", \"tests/test_ext_viewcode.py::test_linkcode\", \"tests/test_ext_viewcode.py::test_local_source_files\"]"
  },
  "generated": true,
  "with_logs": true,
  "applied": true,
  "test_errored": false,
  "test_timeout": false,
  "resolved": true,
  "log_parse": {
    "tests/test_ext_viewcode.py::test_viewcode_epub_default": "PASSED",
    "tests/test_ext_viewcode.py::test_viewcode_epub_enabled": "PASSED",
    "tests/test_ext_viewcode.py::test_linkcode": "PASSED",
    "tests/test_ext_viewcode.py::test_local_source_files": "PASSED",
    "tests/test_ext_viewcode.py::test_viewcode": "FAILED"
  },
  "eval_report": {
    "FAIL_TO_PASS": {
      "success": [
        "tests/test_ext_viewcode.py::test_viewcode_epub_default"
      ],
      "failure": []
    },
    "PASS_TO_PASS": {
      "success": [
        "tests/test_ext_viewcode.py::test_viewcode_epub_enabled",
        "tests/test_ext_viewcode.py::test_linkcode",
        "tests/test_ext_viewcode.py::test_local_source_files"
      ],
      "failure": []
    },
    "FAIL_TO_FAIL": {
      "success": [],
      "failure": []
    },
    "PASS_TO_FAIL": {
      "success": [],
      "failure": []
    }
  }
}

Please refer to EVAL_PATCH.md if you want to learn more about how to evaluate patches that are already generated (e.g., not by OpenDevin).

View Result Summary

If you just want to know the resolve rate, and/or a summary of what tests pass and what don't, you could run

poetry run python ./evaluation/swe_bench/scripts/summarise_results.py <path_to_output_merged_jsonl_file>
# e.g. poetry run python ./evaluation/swe_bench/scripts/summarise_results.py ./evaluation/evaluation_outputs/outputs/swe_bench_lite/CodeActSWEAgent/gpt-4o-2024-05-13_maxiter_50_N_v1.5-no-hint/output.merged.jsonl

Submit your evaluation results

You can start your own fork of our huggingface evaluation outputs and submit a PR of your evaluation results following the guide here.