scholarly journals AVIO: Detecting Atomicity Violations via Access-Interleaving Invariants

IEEE Micro ◽  
2007 ◽  
Vol 27 (1) ◽  
pp. 26-35 ◽  
Author(s):  
Shan Lu ◽  
Joseph Tucek ◽  
Feng Qin ◽  
Yuanyuan Zhou
Keyword(s):  
Atomicity Violations

Detecting Atomicity Violations for Event-Driven Node.js Applications

2019 ◽  
Author(s):  
Xiaoning Chang ◽  
Wensheng Dou ◽  
Yu Gao ◽  
Jie Wang ◽  
Jun Wei ◽  
...  
Keyword(s):  
Event Driven ◽  
Atomicity Violations

BAVC: Classifying Benign Atomicity Violations via Machine Learning

2013 ◽  
Vol 765-767 ◽  
pp. 1576-1580 ◽  
Author(s):  
Qi Chang Chen ◽  
Zhan Fang Chen ◽  
Zhuang Liu ◽  
Xin Feng ◽  
Zhen Gang Jiang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Code Region ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Concurrent Programs ◽  
Testing And Debugging ◽  
Atomicity Violation ◽  
Learning Techniques ◽  
Concurrency Errors ◽  
Atomicity Violations

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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