Abstract-As

Abstract-As

Abstract-As

Test suite augmentation techniques are used in regression testing to help engineers identify code elements affected by changes, and generate test cases to cover those elements. Researchers have created various approaches to identify affected code elements, but only recently have they considered integrating, with this task, approaches for generating test cases. In this paper we explore the use of genetic algorithms in test suite augmentation. We identify several factors that impact the effectiveness of this approach, and we present the results of a case study exploring the effects of one of these factors: the manner in which existing and newly generated test cases are utilized by the genetic algorithm. Our results reveal several ways in which this factor can influence augmentation results, and reveal open problems that researchers must address if they wish to create augmentation techniques that make use of genetic algorithms.

Abstract-Test suite augmentation techniques are used in regression testing to identify code elements affected by changes and to generate test cases to cover those elements. In previous work, we studied two approaches to augmentation, one using a concolic test case generation algorithm and one using a genetic test case generation algorithm. We found that these two approaches behaved quite differently in terms More recently, we empirically studied several factors that can affect augmentation techniques [34]. These include (1) the order in which affected elements are considered while generating test cases, (2) the manner in which existing and newly generated test cases are used, and (3) the algorithm used to generate test cases. The results of our studies show that the primary factor affecting augmentation is the test case