Data Mining Based Optimization of Test Cases to Enhance the Reliability of the Testing

Abstract

Software testing is any activity aimed at evaluating an attribute or capability of a program or system and determining that it meets its required results. Software testing is important activity in Software Development Life Cycle. Test case selection is a crucial activity in testing since the number of automatically generated test cases is usually enormous and possibly unfeasible. Also, a considerable number of test cases are redundant, that is, they exercise similar features of the application and/or are capable of uncovering a similar set of faults. The strategy is aimed at selecting the less similar test cases while providing the best possible coverage of the functional model from which test cases are generated. Test suite selection techniques reduce the effort required for testing by selecting a subset of test suites. In previous work, the problem has been considered as a single-objective optimization problem. However, real world testing can be a complex process in which multiple testing criteria and constraints are involved. The paper utilizes a hybrid, multi-objective algorithm that combines the efficient approximation of the evolutionary approach with the capability data mining algorithm to produce higher-quality test cases. © 2011 Springer-Verlag.