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Generation of Text Suites with Verification and Memetic Algorithms

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IJCA Proceedings on International Conference on Advanced Computer Technology and Development
© 2014 by IJCA Journal
ICACTD - Number 1
Year of Publication: 2014
Authors:
Pulagam Manikanta
R. R. I. Ravi Kumar
S. Maruthu Perumal

Pulagam Manikanta, Ravi R R I Kumar and Maruthu S Perumal. Article: Generation of Text Suites with Verification and Memetic Algorithms. IJCA Proceedings on International Conference on Advanced Computer Technology and Development ICACTD(1):17-21, October 2014. Full text available. BibTeX

@article{key:article,
	author = {Pulagam Manikanta and R. R. I. Ravi Kumar and S. Maruthu Perumal},
	title = {Article: Generation of Text Suites with Verification and Memetic Algorithms},
	journal = {IJCA Proceedings on International Conference on Advanced Computer Technology and Development},
	year = {2014},
	volume = {ICACTD},
	number = {1},
	pages = {17-21},
	month = {October},
	note = {Full text available}
}

Abstract

Genetic Algorithms have been successfully applied to the generation of unit tests for classes, and are well suited to create complex objects through sequences of method calls. However, because the neighborhood in the search space for method sequences is huge, even supposedly simple optimizations on primitive variables (e. g. , numbers and strings) can be ineffective or unsuccessful. To overcome this problem, we extend the global search applied in the EvoSuite test generation tool with local search on the individual statements of method sequences. In contrast to previous work on local search, we also consider complex data types including strings and arrays. Arigorous experimental methodology has been applied to properly evaluate these new local search operators. In our experiments on a set of open source classes of different kinds (e. g. , numerical applications and text processing), the resulting test data generation technique increased branch coverage by up to 32% on average over the normal Genetic Algorithm. A common scenario in software testing is therefore that test data are generated, and a tester manually adds test oracles. As this is a difficult task, it is important to produce small yet representative test sets, and this representativeness is typically measured using code coverage. There is, however, a fundamental problem with the common approach of targeting one coverage goal at a time: Coverage goals are not independent, not equally difficult, and sometimes infeasible the result of test generation is therefore dependent on the order of coverage goals and how many of them are feasible. To overcome this problem, we propose a novel paradigm in which whole test suites are evolved with the aim of covering all coverage goals at the same time while keeping the total size as small as possible. This approach has several advantages, as for example, its effectiveness is not affected by the number of infeasible targets in the code. We have implemented this novel approach in the EVOSUITE tool, and compared it to the common approach of addressing one goal at a time. Evaluated on open source libraries and an industrial case study for a total of 1,741 classes we show that EVOSUITE achieved up to 188 times the branch coverage of a traditional approach targeting single branches, with up to 62 percent smaller test suites.

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