期刊名称：International Journal of Software Engineering and Its Applications
印刷版ISSN：1738-9984
出版年度：2010
卷号：4
期号：4
出版社：SERSC
摘要：Evolutionary testing is a very popular domain in the field of search based software engineering that consists in automatically generating test data for a given piece of code using evolutionary algorithms. One of the most important measures used to evaluate the quality of the generated test suites is code coverage. In this paper we first analyze if there exists a correlation between some static measures computed on the test program and the code coverage when an evolutionary test data generator is used. In particular, we use and compare three techniques for the search engine of the test data generator: an Evolutionary Strategy, a Genetic Algorithm, and a Random Search. We have also developed a program generator that is able to create Java programs with the desired values for the given static measures. Our experimental study includes a benchmark of 1800 programs automatically generated. In addition to the correlations study we also analyze the subset of programs for which one algorithm is better than another one. This second analysis could be the basis for the development of a software tool that automatically decides the suitable test data generation search engine according to the static measures computed on the test object.
