2017 |
Ferrer, Javier; Chicano, Francisco; Alba, Enrique Hybrid algorithms based on integer programming for the search of prioritized test data in software product lines Inproceedings Squillero, Giovanni; Sim, Kevin (Ed.): Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 3–19, Springer International Publishing, Cham, 2017, ISSN: 16113349. Abstract | Links | BibTeX | Tags: Combinatorial Interaction Testing, Feature Models, Integer linear programming, Integer nonlinear programming, Pairwise Testing, prioritization, Software Product Lines @inproceedings{Ferrer2017, title = {Hybrid algorithms based on integer programming for the search of prioritized test data in software product lines}, author = {Javier Ferrer and Francisco Chicano and Enrique Alba}, editor = {Giovanni Squillero and Kevin Sim}, url = {http://dx.doi.org/10.1007/978-3-319-55792-2_1}, doi = {10.1007/978-3-319-55792-2_1}, issn = {16113349}, year = {2017}, date = {2017-01-01}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {10200 LNCS}, pages = {3--19}, publisher = {Springer International Publishing}, address = {Cham}, abstract = {In Software Product Lines (SPLs) it is not possible, in general, to test all products of the family. The number of products denoted by a SPL is very high due to the combinatorial explosion of features. For this reason, some coverage criteria have been proposed which try to test at least all feature interactions without the necessity to test all products, e.g., all pairs of features (pairwise coverage). In addition, it is desirable to first test products composed by a set of priority features. This problem is known as the Prioritized Pairwise Test Data Generation Problem. In this work we propose two hybrid algorithms using Integer Programming (IP) to generate a prioritized test suite. The first one is based on an integer linear formulation and the second one is based on a integer quadratic (nonlinear) formulation. We compare these techniques with two state-of- the-art algorithms, the Parallel Prioritized Genetic Solver (PPGS) and a greedy algorithm called prioritized-ICPL. Our study reveals that our hybrid nonlinear approach is clearly the best in both, solution quality and computation time. Moreover, the nonlinear variant (the fastest one) is 27 and 42 times faster than PPGS in the two groups of instances analyzed in this work. © Springer International Publishing AG 2017.}, keywords = {Combinatorial Interaction Testing, Feature Models, Integer linear programming, Integer nonlinear programming, Pairwise Testing, prioritization, Software Product Lines}, pubstate = {published}, tppubtype = {inproceedings} } In Software Product Lines (SPLs) it is not possible, in general, to test all products of the family. The number of products denoted by a SPL is very high due to the combinatorial explosion of features. For this reason, some coverage criteria have been proposed which try to test at least all feature interactions without the necessity to test all products, e.g., all pairs of features (pairwise coverage). In addition, it is desirable to first test products composed by a set of priority features. This problem is known as the Prioritized Pairwise Test Data Generation Problem. In this work we propose two hybrid algorithms using Integer Programming (IP) to generate a prioritized test suite. The first one is based on an integer linear formulation and the second one is based on a integer quadratic (nonlinear) formulation. We compare these techniques with two state-of- the-art algorithms, the Parallel Prioritized Genetic Solver (PPGS) and a greedy algorithm called prioritized-ICPL. Our study reveals that our hybrid nonlinear approach is clearly the best in both, solution quality and computation time. Moreover, the nonlinear variant (the fastest one) is 27 and 42 times faster than PPGS in the two groups of instances analyzed in this work. © Springer International Publishing AG 2017. |
2012 |
Ferrer, Javier; Kruse, Peter M; Chicano, Francisco; Alba, Enrique Evolutionary algorithm for prioritized pairwise test data generation Inproceedings Soule, Terence; Moore, Jason H (Ed.): łdots on Genetic and evolutionary łdots, pp. 1213–1220, ACM, New York, New York, USA, 2012, ISBN: 978-1-4503-1177-9. Abstract | Links | BibTeX | Tags: combinatorial testing, evolutionary algorithm, pair-, pairwise coverage, prioritization, search based soft-, search based software engineering, software testing, Testing Funcional, ware engineering @inproceedings{DBLP:conf/gecco/FerrerKCA12, title = {Evolutionary algorithm for prioritized pairwise test data generation}, author = {Javier Ferrer and Peter M Kruse and Francisco Chicano and Enrique Alba}, editor = {Terence Soule and Jason H Moore}, url = {http://dl.acm.org/citation.cfm?id=2330163.2330331 http://dl.acm.org/citation.cfm?doid=2330163.2330331 http://dl.acm.org/citation.cfm?id=2330331}, doi = {10.1145/2330163.2330331}, isbn = {978-1-4503-1177-9}, year = {2012}, date = {2012-07-01}, booktitle = {łdots on Genetic and evolutionary łdots}, pages = {1213--1220}, publisher = {ACM}, address = {New York, New York, USA}, abstract = {Combinatorial Interaction Testing (CIT) is a technique used to discover faults caused by parameter interactions in highly configurable systems. These systems tend to be large and exhaustive testing is generally impractical. Indeed, when the resources are limited, prioritization of test cases is a must. Important test cases are assigned a high priority and should be executed earlier. On the one hand, the prioritization of test cases may reveal faults in early stages of the testing phase. But, on the other hand the generation of minimal test suites that fulfill the demanded coverage criteria is an NP-hard problem. Therefore, search based approaches are required to find the (near) optimal test suites. In this work we present a novel evolutionary algorithm to deal with this problem. The experimental analysis compares five techniques on a set of benchmarks. It reveals that the evolutionary approach is clearly the best in our comparison. The presented algorithm can be integrated into CTE XL professional tool.}, keywords = {combinatorial testing, evolutionary algorithm, pair-, pairwise coverage, prioritization, search based soft-, search based software engineering, software testing, Testing Funcional, ware engineering}, pubstate = {published}, tppubtype = {inproceedings} } Combinatorial Interaction Testing (CIT) is a technique used to discover faults caused by parameter interactions in highly configurable systems. These systems tend to be large and exhaustive testing is generally impractical. Indeed, when the resources are limited, prioritization of test cases is a must. Important test cases are assigned a high priority and should be executed earlier. On the one hand, the prioritization of test cases may reveal faults in early stages of the testing phase. But, on the other hand the generation of minimal test suites that fulfill the demanded coverage criteria is an NP-hard problem. Therefore, search based approaches are required to find the (near) optimal test suites. In this work we present a novel evolutionary algorithm to deal with this problem. The experimental analysis compares five techniques on a set of benchmarks. It reveals that the evolutionary approach is clearly the best in our comparison. The presented algorithm can be integrated into CTE XL professional tool. |
2017 |
Hybrid algorithms based on integer programming for the search of prioritized test data in software product lines Inproceedings Squillero, Giovanni; Sim, Kevin (Ed.): Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 3–19, Springer International Publishing, Cham, 2017, ISSN: 16113349. |
2012 |
Evolutionary algorithm for prioritized pairwise test data generation Inproceedings Soule, Terence; Moore, Jason H (Ed.): łdots on Genetic and evolutionary łdots, pp. 1213–1220, ACM, New York, New York, USA, 2012, ISBN: 978-1-4503-1177-9. |