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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| Vydáno v: | The Journal of systems and software Ročník 83; číslo 12; s. 2618 - 2626 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Elsevier Inc
01.12.2010
Elsevier Sequoia S.A Elsevier |
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| 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. |
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| 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. [PUBLICATION 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. |
| Author | Gotlieb, Arnaud Petit, Matthieu |
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| Cites_doi | 10.1109/TSE.1984.5010257 10.1002/stvr.333 10.1109/TSE.1975.6312836 10.1109/TSE.1976.233816 10.1109/32.62448 10.1016/S0743-1066(98)10006-7 10.2307/2347988 |
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| Keywords | Random testing Path testing Constraint reasoning Path Testing |
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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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