Semantic Code Search

Semantic code search implementation using Tensorflow framework and the source code data from the CodeSearchNet project. The model training pipeline was based on the implementation in CodeSearchNet repository. Python, Java, Go, Php, Javascript, and Ruby programming language are supported.

Model Description

BPE tokenizer is used to encode both code strings and query strings(docstrings are used as queries in training). Code strings are padded and encoded to the length of 200 tokens. Query strings are padded and encoded to the length of 30 tokens. Both code embedding size and query embedding size are 256. Token embeddings are masked and then an unweighted mean is performed to get a vector with 256 dimensions for code strings and query strings. Cosine similarity is calculated between the code representations and the query representations. Further details can be found on the WANDB run

Model Structure

• Deep Structured Semantic Model
• Wide & Deep Learning

Project Structure

Python package with scripts to prepare the data, train/test the model and predict.

Data

We use the data from the CodeSearchNet project. The downloaded data is around 20GB.

Training the model

To install the reqiured dependencies

pip3 install -r requirements.txt

Preparing data

Data preparation step is seperated from the training step because of computing time and memory consumption.

Training and evaluation

Start the training

python3 -m train --model neuralbow_v1

The model will be trained for each language. The evaluation metric is MRR for validation and test sets, however, the output of prediction will be evaluated by GitHub using nDCG.

Model Downloads

• Hybrid Model

Query the trained model

Predict

python3 predict.py -r wuchen/SemanticCodeSearch/1fpfl6dq

Online Semantic Code Search website

• Requirements: Flask
• Import source code file
• Running the dev server

Model Server

python3 -m server.main

Web Front-End

cd react-code-search
npm install
npm start