A Novel Evolutionary Approach for Adaptive Random Testing

Random testing is a low cost strategy that can be applied to a wide range of testing problems. While the cost and straightforward application of random testing are appealing, these benefits must be evaluated against the reduced effectiveness due to the generality of the approach. Recently, a number...

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Bibliographic Details
Published in:IEEE transactions on reliability Vol. 58; no. 4; pp. 619 - 633
Main Authors: Tappenden, A.F., Miller, J.
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2009
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
Adaptive random testing
Algorithms
Analysis of variance
Application software
Asymptotic properties
automated testing
Automatic testing
Biological cells
Complexity
Costs
Evolutionary
Evolutionary algorithms
evolutionary computing and genetic algorithms
Gain
Genetic algorithms
Power capacitors
random testing
Runtime
Search algorithms
Software testing
Strategy
Studies
Subspace constraints
test generation
test strategies
ISSN:0018-9529, 1558-1721
Online Access:Get full text
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Summary:Random testing is a low cost strategy that can be applied to a wide range of testing problems. While the cost and straightforward application of random testing are appealing, these benefits must be evaluated against the reduced effectiveness due to the generality of the approach. Recently, a number of novel techniques, coined Adaptive Random Testing, have sought to increase the effectiveness of random testing by attempting to maximize the testing coverage of the input domain. This paper presents the novel application of an evolutionary search algorithm to this problem. The results of an extensive simulation study are presented in which the evolutionary approach is compared against the Fixed Size Candidate Set (FSCS), Restricted Random Testing (RRT), quasi-random testing using the Sobol sequence (Sobol), and random testing (RT) methods. The evolutionary approach was found to be superior to FSCS, RRT, Sobol, and RT amongst block patterns, the arena in which FSCS, and RRT have demonstrated the most appreciable gains in testing effectiveness. The results among fault patterns with increased complexity were shown to be similar to those of FSCS, and RRT; and showed a modest improvement over Sobol, and RT. A comparison of the asymptotic and empirical runtimes of the evolutionary search algorithm, and the other testing approaches, was also considered, providing further evidence that the application of an evolutionary search algorithm is feasible, and within the same order of time complexity as the other adaptive random testing approaches.
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ISSN:0018-9529
1558-1721
DOI:10.1109/TR.2009.2034288