leonyang95 / cedar

Code Example Demonstration Automated Retrieval (CEDAR)

Paper

Retrieval-Based Prompt Selection for Code-Related Few-Shot Learning, Published at IEEE/ACM International Conference on Software Engineering (ICSE), 2023.

Setup environment

• Prerequisite:

  • Install conda package manager.

• Create python environment with required dependencies (for example, openai).

# The following steps create an isolated environment codex-env and install the required dependencies.

conda create --name codex-env python=3.9 # creates the codex-env environment
conda activate codex-env                 # activates the codex-env environment
conda install openai                     # installs dependency - openai

pip install backoff
pip install edit_distance
conda install difflib
conda install matplotlib
conda install plotly
conda install scipy
conda install sklearn

pip install tenacity
pip install suffix-trees
pip install lizard
conda install gensim
pip install rank_bm25

• We have to compress large files in order to upload them to GitHub.
  • Please uncompress these files before running the code.
  • For example, uncompress this file before running the code.

Setup environment for embedding search

We use sentence-transformers to generate embeddings for the code snippets.

conda create -n semantic-embedding --file embedding-prereq.txt # create the semantic-embedding environment
conda install -c pytorch faiss-cpu # install faiss-cpu

To install sentence-transformers, please follow the instructions from here. For linux environment, as stated in the above link, sentence-transformers gets installed using the following command: pip install -U sentence-transformers.

However, for mac with m1 chip, we had to run the following commands to get it installed:

conda list openmp
conda unistall intel-openmp
conda install -c conda-forge sentence-transformers

Install vector database

We use vector database lite (like SQLITE but for vector search). vdblite library details could be found here.

pip install vdblite

Running experiment

Set OpenAI key:

export OPENAI_API_KEY=<your-key> 

Run the following script, to run the experiments for atlas:

python main_atlas.py

Run the following script, to run the experiments for tfix:

python main_tfix.py

For all the different configurations, please use command line parameters accordingly. More details about the command line parameters could be found in the main_atlas.py and main_tfix.py files. Also, each folder contains a README file with more details.

Running evaluation

After running the experiments, results are saved in the folder: ./codex/framework/results/.

Run the following script, to see the evaluation metrics.

python evaluation/result_analysis_atlas.py ./results.csv