DeGraphCS

Project Overview

This project provides a collection of datasets and source code which are used in our DeGraphCS model. The content of project is as follows:

1. Dataset

2. DeGraphCS Source Code

3. Variable-based Flow Graph Construction

4. Baseline methods

5. User Study

6. Appendix

Dataset

To help people to reproduce our work, we provide raw datasets which are consist of C code snippet, corresponding code comment and generated IR.

The raw datasets can be accessed in Google Drive

To feed into our model, we first generate Variable-based Flow Graph of 41152 methods and extract corresponding comments. Then we split the datasets into 39152 training set and 2000 test set. All of the data are puted in dataset/ directory.

DeGraphCS Source Code

We provide DeGraphCS model code which are listed in src/ directory.

Variable-based Flow Graph Construction

To construct Variable-based Flow Graph according to llvm IR, We provide graph construction code to help users to generate graph which are puted in IR2graph/ directory.

Baseline Methods

We have reproduced other code search works which are putted in Baseline methods/ directory.

User Study

We make a user study to evaluate our model.

50 queries of the user study are listed in the user study/queries.txt. And according to four models (UNIF, MMAN, DeepCS and DeGraphCS), we obtain corresponding searching result which are listed in user study/ directory.

Appendix

Running Our Model

Generate Datasets and Build Dictionary

Run the command to split comments datasets into training set and test set, and build dictionary

python src/util_desc.py

Run the command to split Variable-based Flow Graph datasets into training set and test set, and build dictionary

python src/util_ir.py

Train the DeGraphCS Model

python src/train.py

Test the DeGraphCS Model

python src/test.py