Automatic Generation of Unit Tests for Correlated Variables in Parallel Programs

A notorious class of concurrency bugs are race condition related to correlated variables, which make up about 30 % of all non-deadlock concurrency bugs. A solution to prevent this problem is the automatic generation of parallel unit tests. This paper presents an approach to generate parallel unit te...

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Vydáno v:International journal of parallel programming Ročník 44; číslo 3; s. 644 - 662
Hlavní autoři: Jannesari, Ali, Wolf, Felix
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.06.2016
Springer Nature B.V
Témata:
Analysis
Automation
Computer programming
Computer Science
Concurrency
Correlation
Debugging
Estimates
Generators
Parallel processing
Parallel programming
Processor Architectures
Race
Redundant
Semantics
Software
Software Engineering/Programming and Operating Systems
Studies
Theory of Computation
Variables
Unit tests
Correlated variables
Parallel programming
Automatic testing
Race detection
Debugging
Program analysis
ISSN:0885-7458, 1573-7640
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Shrnutí:A notorious class of concurrency bugs are race condition related to correlated variables, which make up about 30 % of all non-deadlock concurrency bugs. A solution to prevent this problem is the automatic generation of parallel unit tests. This paper presents an approach to generate parallel unit tests for variable correlations in multithreaded code. We introduce a hybrid approach for identifying correlated variables. Furthermore, we estimate the number of potentially violated correlations for methods executed in parallel. In this way, we are capable of creating unit tests that are suited for race detectors considering correlated variables. We were able to identify more than 85 % of all race conditions on correlated variables in eight applications after applying our parallel unit tests. At the same time, we reduced the number of unnecessary generated unit tests. In comparison to a test generator unaware of variable correlations, redundant unit tests are reduced by up to 50 %, while maintaining the same precision and accuracy in terms of the number of detected races.
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ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-015-0363-8