ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2022, Vol. 59 ›› Issue (9): 1914-1928.doi: 10.7544/issn1000-1239.20220014

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Deep Learning Based Data Race Detection Approach

Zhang Yang, Qiao Liu, Dong Chunhao, Gao Hongbin   

  1. (College of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018)
  • Online:2022-09-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61440012), the Key Scientific Research Project of Hebei Education Department (ZD2019093), the Scientific Support Project of Hebei Province (16210312D), and the Innovative Ability Foundation for Graduates of Hebei Province (CXZZSS2022081).

Abstract: Existing approaches for deep-learning-based data race detection are suffering from the issues of single feature extraction and low accuracy. To improve the state-of-the-art, a novel approach called DeleRace is proposed to detect data race based on deep learning model. Firstly, DeleRace extracts instruction-level, method-level, and file-level features from a variety of real-world applications based on static analysis tool WALA. All these features are transformed by word vectorization to build the training dataset. Secondly, ConRacer, as an existing data race tool, is employed to identify the real race. Based on this tool, those positive samples in the training dataset is labelled. To further optimize the dataset, DeleRace leverages SMOTE algorithm to distribute both positive samples and negative ones in balance. Finally, CNN-LSTM model is constructed and a classifier is trained to detect data race. In the experimentation, a total of 26 real-world applications is selected from different fields in DaCapo, JGF, IBM Contest and PJBench benchmark suites. The experimental results show that the accuracy of DeleRace is 96.79% which is 4.65% higher than existing deep-learning-based approaches. Furthermore, the performance of DeleRace is compared with that of both dynamic tools (such as Said and RVPredict) and static tools (such as SRD and ConRacer), which demonstrates the effectiveness of DeleRace.

Key words: data race, concurrent program, deep learning, feature extraction, CNN-LSTM model

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