DeepRace: Finding Data Race Bugs via Deep Learning

TL;DR

DeepRace employs a deep learning model to detect data race bugs in source code at the file and line level, achieving high accuracy and effective localization.

Contribution

This paper introduces DeepRace, a novel CNN-based approach that automatically detects and localizes data race bugs in source code without manual feature engineering.

Findings

Achieved 81-86% accuracy in classifying buggy files.

Successfully localized buggy lines with 66% intersection-over-union.

Performed well on large datasets with minimal false positives.

Abstract

With the proliferation of multi-core hardware, parallel programs have become ubiquitous. These programs have their own type of bugs known as concurrency bugs and among them, data race bugs have been mostly in the focus of researchers over the past decades. In fact, detecting data races is a very challenging and important task. There have been several research paths in this area with many sophisticated tools designed and utilized that focus on detecting data race at the file level. In this paper, we propose DeepRace, a novel approach toward detecting data races in the source code. We build a deep neural network model to find data races instead of creating a data race detector manually. Our model uses a one-layer convolutional neural network (CNN) with different window size to find data races method. Then we adopt the class activation map function with global average pooling to extract the weights of the last convolutional layer and backpropagate it with the input source code to extract the line of codes with a data race. Thus, the DeepRace model can detect the data race bugs on a file and line of code level. In addition, we noticed that DeepRace successfully detects several buggy lines of code at different locations of the file. We tested the model with OpenMP and POSIX source code datasets which consist of more than 5000 and 8000 source code files respectively. We were able to successfully classify buggy source code files and achieve accuracies ranging from 81% and 86%. We also measured the performance of detecting and visualizing the data race at the line of code levels and our model achieved promising results. We only had a small number of false positives and false, ranging from 1 to 10. Furthermore, we used the intersection of union to measure the accuracy of the buggy lines of code, our model achieved promising results of 66 percent.