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A.: Evolutionary generation of whole test suites (2011)
| Venue: | In: International Conference On Quality Software (QSIC |
| Citations: | 25 - 17 self |
BibTeX
@INPROCEEDINGS{Fraser11a.:evolutionary,
author = {Gordon Fraser and Andrea Arcuri},
title = {A.: Evolutionary generation of whole test suites},
booktitle = {In: International Conference On Quality Software (QSIC},
year = {2011},
pages = {31--40}
}
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Abstract
Abstract—Recent advances in software testing allow automatic derivation of tests that reach almost any desired point in the source code. There is, however, a fundamental problem with the general idea of targeting one distinct test coverage goal at a time: Coverage goals are neither independent of each other, nor is test generation for any particular coverage goal guaranteed to succeed. We present EVOSUITE, a search-based approach that optimizes whole test suites towards satisfying a coverage criterion, rather than generating distinct test cases directed towards distinct coverage goals. Evaluated on five open source libraries and an industrial case study, we show that EVOSUITE achieves up to 18 times the coverage of a traditional approach targeting single branches, with up to 44 % smaller test suites. Keywords-Search based software engineering, length, branch coverage, genetic algorithm
Keyphrases
whole test suite evolutionary generation coverage goal distinct test case particular coverage goal present evosuite genetic algorithm distinct test coverage goal single branch search-based approach source code allow automatic derivation towards distinct coverage goal coverage criterion open source library desired point general idea abstract recent advance software engineering test suite fundamental problem traditional approach branch coverage industrial case study test generation
