Paper Info
Title
Multi-objective Genetic Optimization for Noise-Based Testing of Concurrent Software.
Abstract
Testing of multi-threaded programs is a demanding work due to the many possible thread interleavings one should examine. The noise injection technique helps to increase the number of thread interleavings examined during repeated test executions provided that a suitable setting of noise injection heuristics is used. The problem of finding such a setting, i.e., the so called test and noise configuration search problem (TNCS problem), is not easy to solve. In this paper, we show how to apply a multi-objective genetic algorithm (MOGA) to the TNCS problem. In particular, we focus on generation of TNCS solutions that cover a high number of distinct interleavings (especially those which are rare) and provide stable results at the same time. To achieve this goal, we study suitable metrics and ways how to suppress effects of non-deterministic thread scheduling on the proposed MOGA-based approach. We also discuss a choice of a concrete MOGA and its parameters suitable for our setting. Finally, we show on a set of benchmark programs that our approach provides better results when compared to the commonly used random approach as well as to the sooner proposed use of a single-objective genetic approach.
Year
DOI
Venue
2014
10.1007/978-3-319-09940-8_8
Lecture Notes in Computer Science
Field
DocType
Volume
System under test,Computer science,Software,Software reliability testing,Heuristics,Artificial intelligence,Search problem,Software construction,Computer engineering,Machine learning,Genetic algorithm,Search-based software engineering
Conference
8636
ISSN
Citations 
PageRank 
0302-9743
4
0.38
References 
Authors
27
5
Name
Order
Citations
PageRank
Vendula Hrubá1101.54
Bohuslav Krena2422.54
Zdenek Letko3361.78
Hana Pluhácková451.08
Tomás Vojnar513627.58