😺 Agentless

😺 About

Agentless is an agentless approach to automatically solve software development problems. To solve each issue, Agentless follows a simple two phase process: localization and repair.

🙀 Localization: Agentless employs a hierarchical process to first localize the fault to specific files, then to relevant classes or functions, and finally to fine-grained edit locations
😼 Repair : Agentless takes the edit locations and generates multiple candidate patches in a simple diff format, performs test filtering, and re-ranks all remaining patches to selects one to submit

🐈 Setup

First create the environment

git clone https://github.com/OpenAutoCoder/Agentless.git
cd Agentless

conda create -n agentless python=3.11 
conda activate agentless
pip install -r requirements.txt
export PYTHONPATH=$PYTHONPATH:$(pwd)

⏬ Developer Setup

# for contribution, please install the pre-commit hook.
pre-commit install  # this allows a more standardized code style

Then export your OpenAI API key

export OPENAI_API_KEY={key_here}

Now you are ready to run Agentless on the problems in SWE-bench! We now go through a step-by-step example of how to run Agentless.

Note

To reproduce the full SWE-bench lite experiments and follow our exact setup as described in the paper. Please see this README

🙀 Localization

Tip

For localization, you can use --target_id to specific a particular bug you want to target.

For example --target_id=django__django-11039

In localization, the goal is find the locations in source code where we need to edit to fix the issues. Agentless uses a 3-stage localization step to first localize to specific files, then to relevant code elements, and finally to fine-grained edit locations.

Tip

Since for each issue in the benchmark we need to checkout the repository and process the files, you might want to save some time by downloading the preprocessed data here: swebench_lite_repo_structure.zip

After downloading, please unzip and export the location as such export PROJECT_FILE_LOC={folder which you saved}

Run the following command to generate the edit locations:

mkdir results # where we will save our results
python agentless/fl/localize.py --file_level --related_level --fine_grain_line_level \
                                --output_folder results/location --top_n 3 \
                                --compress \
                                --context_window=10

This will save all the localized locations in results/location/loc_outputs.jsonl with the logs saved in results/location/localize.log

⏬ Structure of `loc_outputs.jsonl` :: click to expand ::

instance_id: task ID of the issue
found_files: list of files localized by the model
additional_artifact_loc_file: raw output of the model during file-level localization
file_traj: trajectory of the model during file-level localization (e.g., # of tokens)
found_related_locs: list of relevant code elements localized by the model
additional_artifact_loc_related: raw output of the model during relevant-code-level localization
related_loc_traj: trajectory of the model during relevant-code-level localization
found_edit_locs: list of edit locations localized by the model
additional_artifact_loc_edit_location: raw output of the model during edit-location-level localization
edit_loc_traj: trajectory of the model during edit-location-level localization

🙀 Individual localization steps :: click to perform the individual localization step ::

Localize to files

We first start by localization to specific files

mkdir results # where we will save our results
python agentless/fl/localize.py --file_level --output_folder results/file_level

This command saves the file-level localization in results/file_level/loc_outputs.jsonl, you can also check results/file_level/localize.log for detailed logs

Localize to related elements

Next, we localize to related elements within each of the files we localize

python agentless/fl/localize.py --related_level \
                                --output_folder results/related_level \
                                --start_file results/file_level/loc_outputs.jsonl \
                                --top_n 3 --compress

Here the --start_file refers to the previous file-level localization. --top_n argument indicates the number of files we want to consider.

Similar to the previous stage, this command saves the related-element localization in results/related_level/loc_outputs.jsonl, with logs in results/related_level/localize.log

Localize to edit locations

Finally, we take the related elements from the previous step and localize to the edit locations we want the LLM to generate patches for

python agentless/fl/localize.py --fine_grain_line_level \
                                --output_folder results/edit_location \
                                --start_file results/related_level/loc_outputs.jsonl \
                                --top_n 3 --context_window=10

Here the --start_file refers to the previous related-element localization. --context_window indicates the amount of lines before and after we provide to the LLM.

The final edit locations Agentless will perform repair on is saved in results/edit_location/loc_outputs.jsonl, with logs in results/edit_location/localize.log

Sampling edit locations multiple times and merging

For the last localization step of localizing to edit locations, we can also perform sampling to obtain multiple sets of edit locations.

python agentless/fl/localize.py --fine_grain_line_level \
                                --output_folder results/edit_location_samples \
                                --start_file results/related_level/loc_outputs.jsonl \
                                --top_n 3 --context_window=10 --temperature 0.8 \
                                --num_samples 4

This command will sample with temperature 0.8 and generate 4 edit location sets. We can then merge them together to form a bigger list of edit locations.

Run the following command to merge:

python agentless/fl/localize.py --merge \
                                --output_folder results/edit_location_samples_merged \
                                --start_file results/edit_location_samples/loc_outputs.jsonl \
                                --num_samples 4

This will perform pair-wise merging of samples (i.e., sample 0 and 1 will be merged and sample 2 and 3 will be merged). Furthermore it will also merge all samples together.

The merged location files can be found in results/edit_location_samples_merged/loc_merged_{st_id}-{en_id}_outputs.jsonl where st_id and en_id indicates the samples that are being merged. The location file with all samples merged together can be found as results/edit_location_samples_merged/loc_all_merged_outputs.jsonl. Furthermore, we also include the location of each individual sample for completeness within the folder.

😼 Repair

Using the edit locations (i.e., found_edit_locs) from before, we now perform repair.

Agentless generates multiple patches per issue (controllable via parameters) and then perform majority voting to select the final patch for submission

Run the following command to generate the patches:

python agentless/repair/repair.py --loc_file results/location/loc_outputs.jsonl \
                                  --output_folder results/repair \
                                  --loc_interval --top_n=3 --context_window=10 \
                                  --max_samples 10  --cot --diff_format \
                                  --gen_and_process

This command generates 10 samples (1 greedy and 9 via temperature sampling) as defined --max_samples 10. The --context_window indicates the amount of code lines before and after each localized edit location we provide to the model for repair. The repair results is saved in results/repair/output.jsonl, which contains the raw output of each sample as well as the any trajectory information (e.g., number of tokens). The complete logs are also saved in results/repair/repair.log

Note

We also perform post-processing to generate the complete git-diff patch for each repair samples.

You can find the individual patch in results/repair/output_{i}_processed.jsonl where i is the sample number.

Finally, we perform majority voting to select the final patch to solve each issue. Run the following command:

python agentless/repair/rerank.py --patch_folder results/repair --num_samples 10 --deduplicate --plausible

In this case, we use --num_samples 10 to pick from the 10 samples we generated previously, --deduplicate to apply normalization to each patch for better voting, and --plausible to select patches that can pass the previous regression tests (warning: this feature is not yet implemented)

This command will produced the all_preds.jsonl that contains the final selected patch for each instance_id which you can then directly use your favorite way of testing SWE-bench for evaluation!

🐈‍⬛ Artifacts

You can download the complete artifacts of Agentless in our v0.1.0 release:

🐈‍⬛ agentless_logs: raw logs and trajectory information
🐈‍⬛ swebench_lite_repo_structure: preprocessed structure information for each SWE-Bench-lite problem
🐈‍⬛ 20240630_agentless_gpt4o: evaluated run of Agentless used in our paper

📝 Citation

@article{agentless,
  author    = {Xia, Chunqiu Steven and Deng, Yinlin and Dunn, Soren and Zhang, Lingming},
  title     = {Agentless: Demystifying LLM-based Software Engineering Agents},
  year      = {2024},
  journal   = {arXiv preprint},
}

Note

The first two authors contributed equally to this work, with author order determined via Nigiri

jibinmathew69/Agentless

😺 Agentless

😺 About

🐈 Setup

🙀 Localization

Localize to files

Localize to related elements

Localize to edit locations

Sampling edit locations multiple times and merging

😼 Repair

🐈‍⬛ Artifacts

📝 Citation

😽 Acknowledgement