A uniform random test data generator for path testing

Path-oriented Random Testing (PRT) aims at generating a uniformly spread out sequence of random test data that execute a single control flow path within a program. The main challenge of PRT lies in its ability to build efficiently such a test suite in order to minimize the number of rejects (test da...

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Veröffentlicht in:	The Journal of systems and software Jg. 83; H. 12; S. 2618 - 2626
Hauptverfasser:	Gotlieb, Arnaud, Petit, Matthieu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Inc 01.12.2010 Elsevier Sequoia S.A Elsevier
Schlagworte:	Approximation Benchmarks Computer Science Constraint reasoning Construction Divide & conquer algorithms Flow paths Generators Mathematical analysis Path testing Product testing Programming Programming languages Random testing Reasoning Software Engineering Studies Test methods Random testing Path testing Constraint reasoning Path Testing
ISSN:	0164-1212, 1873-1228
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Abstract	Path-oriented Random Testing (PRT) aims at generating a uniformly spread out sequence of random test data that execute a single control flow path within a program. The main challenge of PRT lies in its ability to build efficiently such a test suite in order to minimize the number of rejects (test data that execute another control flow path). We address this problem with an original divide-and-conquer approach based on constraint reasoning over finite domains, a well-recognized Constraint Programming technique. Our approach first derives path conditions by using backward symbolic execution and computes a tight over-approximation of their associated subdomain by using constraint propagation and constraint refutation. Second, a uniform random test data generator is extracted from this approximated subdomain. We implemented this approach and got experimental results that show the practical benefits of PRT based on constraint reasoning. On average, we got a two-order magnitude CPU time improvement over standard Random Testing on a set of paths extracted from classical benchmark programs.
AbstractList	Path-oriented Random Testing (PRT) aims at generating a uniformly spread out sequence of random test data that execute a single control flow path within a program. The main challenge of PRT lies in its ability to build efficiently such a test suite in order to minimize the number of rejects (test data that execute another control flow path). We address this problem with an original divide-and-conquer approach based on constraint reasoning over finite domains, a well-recognized Constraint Programming technique. Our approach first derives path conditions by using backward symbolic execution and computes a tight over-approximation of their associated subdomain by using constraint propagation and constraint refutation. Second, a uniform random test data generator is extracted from this approximated subdomain. We implemented this approach and got experimental results that show the practical benefits of PRT based on constraint reasoning. On average, we got a two-order magnitude CPU time improvement over standard Random Testing on a set of paths extracted from classical benchmark programs. Path-oriented Random Testing (PRT) aims at generating a uniformly spread out sequence of random test data that execute a single control flow path within a program. The main challenge of PRT lies in its ability to build efficiently such a test suite in order to minimize the number of rejects (test data that execute another control flow path). We address this problem with an original divide-and-conquer approach based on constraint reasoning over finite domains, a well-recognized Constraint Programming technique. Our approach first derives path conditions by using backward symbolic execution and computes a tight over-approximation of their associated subdomain by using constraint propagation and constraint refutation. Second, a uniform random test data generator is extracted from this approximated subdomain. We implemented this approach and got experimental results that show the practical benefits of PRT based on constraint reasoning. On average, we got a two-order magnitude CPU time improvement over standard Random Testing on a set of paths extracted from classical benchmark programs. [PUBLICATION ABSTRACT]
Author	Gotlieb, Arnaud Petit, Matthieu
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SubjectTerms	Approximation Benchmarks Computer Science Constraint reasoning Construction Divide & conquer algorithms Flow paths Generators Mathematical analysis Path testing Product testing Programming Programming languages Random testing Reasoning Software Engineering Studies Test methods
Title	A uniform random test data generator for path testing
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