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

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.

BAVC: Classifying Benign Atomicity Violations via Machine Learning

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.