On The Importance of Reasoning for Context Retrieval in Repository-Level Code Editing

This repository contains the code for the paper "On The Importance of Reasoning for Context Retrieval in Repository-Level Code Editing".

We provide a comprehensive evaluation of the context retrieval task with different reasoning strategies.

This framework can be further extended to evaluate other context retrieval models for code editing.

How to Use

Installation

This project uses poetry for dependency management. To install the dependencies, run:

poetry install

To activate the virtual environment, run:

poetry shell

Quick Start

To run the experiments, use the following command:

export PYTHONPATH=.
export CE_DATA_PATH=cache_dir

python code_editing/scripts/run_agent.py

Our framework uses the hydra library for configuration management. You can customize the configuration by modifying the code_editing/scripts/conf directory.

To run the experiments with a specific configuration, use the following command:

python code_editing/scripts/run_agent.py -cn <config_name> tools=<toolkit> data_source=<data_source>

Available configuration names are:

• baseline (bm25 retrieval with unmodified query)
• agent (tool call criterion)
• agent_cl (context length criterion)
• agent_sr (self reflection criterion)
• acr (AutoCodeRover)
• my_acr (AutoCodeRover with langchain-based retrieval)

In addition, different toolkits and data sources can be specified, such as:

• retrieval_search (langchain-based retrieval, bm25 by default)
• acr_toolkit (AST tools from AutoCodeRover)

By default, SWE-Bench_Lite is used as the data source.

For example, to run the experiments with Self-Reflection stopping criterion and AST tools from AutoCodeRover on full SWE-Bench dataset, use the following command:

python code_editing/scripts/run_agent.py -cn agent_sr tools=acr_toolkit data_source=swe_bench data_source.lite=false

More details can be found in the code_editing/scripts/conf and code_editing/configs directories.

Evaluation

To evaluate the results, use the following command:

python code_editing/scripts/evaluate.py input_path=/path/to/inference.jsonl

The evaluation script will output the evaluation metrics for the inference results. To configure the evaluation, modify the code_editing/scripts/conf/evaluation.yaml file.

License

This project is licensed under the MIT License - see the LICENSE file for details.