2nd International Conference On Systems Engineering and Modeling (ICSEM-13)

BAVC: Classifying Benign Atomicity Violations via Machine Learning

Authors
Qichang Chen, Zhanfang Chen, Zhuang Liu, Xin Feng, Zhengang Jiang, Liqiang Wang, Hongyi Ma, Ping Guo
Corresponding Author
Qichang Chen
Available Online April 2013.
DOI
https://doi.org/10.2991/icsem.2013.133How to use a DOI?
Keywords
Atomicity Violations, Concurrency Errors, Machine Learning, Software Testing, Program Analysis
Abstract
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.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2nd International Conference On Systems Engineering and Modeling (ICSEM-13)
Part of series
Advances in Intelligent Systems Research
Publication Date
April 2013
ISBN
978-94-91216-42-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/icsem.2013.133How to use a DOI?
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
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  -