BAVC: Classifying Benign Atomicity Violations via Machine Learning
Qichang Chen, Zhanfang Chen, Zhuang Liu, Xin Feng, Zhengang Jiang, Liqiang Wang, Hongyi Ma, Ping Guo
Available Online April 2013.
- https://doi.org/10.2991/icsem.2013.133How to use a DOI?
- Atomicity Violations, Concurrency Errors, Machine Learning, Software Testing, Program Analysis
- The reality of multi-core hardware has made concurrent programs pervasive. Unfortunately, writing correct concurrent programs is difficult. Atomicity violation, which is caused by concurrent executions unexpectedly violating the atomicity of a certain code region, is one of the most common concurrency errors. However, atomicity violation bugs are hard to find using traditional testing and debugging techniques. In this paper, we investigate an approach based on machine learning techniques (specifically decision tree and support vector machine (SVM)) for classifying the benign atomicity violations from the harmful ones. A benign atomicity violation is known not to affect the program's correctness even it happens. We formulate our problem as a supervised-learning problem and apply these two machine learning techniques to classify the atomicity violation report. Our experimental evaluation shows that the proposed method is effective in identifying the benign atomicity violation warnings.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Qichang Chen AU - Zhanfang Chen AU - Zhuang Liu AU - Xin Feng AU - Zhengang Jiang AU - Liqiang Wang AU - Hongyi Ma AU - Ping Guo PY - 2013/04 DA - 2013/04 TI - BAVC: Classifying Benign Atomicity Violations via Machine Learning BT - 2nd International Conference On Systems Engineering and Modeling (ICSEM-13) PB - Atlantis Press SP - 658 EP - 662 SN - 1951-6851 UR - https://doi.org/10.2991/icsem.2013.133 DO - https://doi.org/10.2991/icsem.2013.133 ID - Chen2013/04 ER -