scholarly journals A Method for Improving the Precision and Coverage of Atomicity Violation Predictions

2015 ◽  
pp. 116-130
Author(s):  
Reng Zeng ◽  
Zhuo Sun ◽  
Su Liu ◽  
Xudong He
Keyword(s):  
Atomicity Violation

Demand Driven Atomicity Violation Analysis with Event Slicing

2012 ◽  
Vol 4 (20) ◽  
pp. 52-60
Author(s):  
Yan Hu ◽  
Zhenquan Qin ◽  
He Jiang ◽  
Weifeng Sun
Keyword(s):  
Atomicity Violation

scholarly journals Atomicity Violation in Multithreaded Applications and Its Detection in Static Code Analysis Process

2020 ◽  
Vol 10 (22) ◽  
pp. 8005
Author(s):  
Damian Giebas ◽  
Rafał Wojszczyk
Keyword(s):  
Parallel Computing ◽  
Source Code ◽  
Code Analysis ◽  
Atomicity Violation ◽  
Code Model ◽  
Analysis Process ◽  
Static Code Analysis ◽  
Violation Detection ◽  
Definition Of

This paper is a contribution to the field of research dealing with the parallel computing, which is used in multithreaded applications. The paper discusses the characteristics of atomicity violation in multithreaded applications and develops a new definition of atomicity violation based on previously defined relationships between operations, that can be used to atomicity violation detection. A method of detection of conflicts causing atomicity violation was also developed using the source code model of multithreaded applications that predicts errors in the software.

Verifying Synchronization for Atomicity Violation Fixing

2016 ◽  
Vol 42 (3) ◽  
pp. 280-296 ◽  
Author(s):  
Qingkai Shi ◽  
Jeff Huang ◽  
Zhenyu Chen ◽  
Baowen Xu
Keyword(s):  
Atomicity Violation

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.

On-the-fly Atomicity Violation Repairing Technique for Airborne Health Management Systems

2020 ◽  
Vol 48 (7) ◽  
pp. 547-554
Author(s):  
Eu-Teum Choi ◽  
Dong-Su Lee ◽  
Yong-Kee Jun ◽  
Seongjin Lee
Keyword(s):  
Health Management ◽  
Management Systems ◽  
Atomicity Violation

Finding Atomicity-Violation Bugs through Unserializable Interleaving Testing

2012 ◽  
Vol 38 (4) ◽  
pp. 844-860 ◽  
Author(s):  
Shan Lu ◽  
Soyeon Park ◽  
Yuanyuan Zhou
Keyword(s):  
Atomicity Violation

scholarly journals Automated atomicity-violation fixing

2011 ◽  
Vol 46 (6) ◽  
pp. 389-400 ◽  
Author(s):  
Guoliang Jin ◽  
Linhai Song ◽  
Wei Zhang ◽  
Shan Lu ◽  
Ben Liblit
Keyword(s):  
Atomicity Violation
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