Dynamic method for software test data generation

Test data generation in program testing is the process of identifying a set of test data which satisfies a given testing criterion. Existing pathwise test data generators proceed by selecting program paths that satisfy the selected criterion and then generating program inputs for these paths. One of the problems with this approach is that unfeasible paths are often selected; as a result, significant computational effort can be wasted in analysing those paths. In this paper, an approach to test data generation, referred to as a dynamic approach for test data generation, is presented. In this approach, the path selection stage is eliminated. Test data are derived based on the actual execution of the program under test and function minimization methods. The approach starts by executing a program for an arbitrary program input. During program execution for each executed branch, a search procedure decides whether the execution should continue through the current branch or an alternative branch should be taken. If an undesirable execution flow is observed at the current branch, then a real-valued function is associated with this branch, and function minimization search algorithms are used to locate values of input variables automatically, which will change the flow of execution at this branch.     

A search-based framework for automatic testing of MATLAB/Simulink models

Search-based test-data generation has proved successful for code-level testing but almost no search-based work has been carried out at higher levels of abstraction. In this paper the application of such approaches at the higher levels of abstraction offered by MATLAB/Simulink models is investigated and a wide-ranging framework for test-data generation and management is presented. Model-level analogues of code-level structural coverage criteria are presented and search-based approaches to achieving them are described. The paper also describes the first search-based approach to the generation of mutant-killing test data, addressing a fundamental limitation of mutation testing. Some problems remain whatever the level of abstraction considered. In particular, complexity introduced by the presence of persistent state when generating test sequences is as much a challenge at the Simulink model level as it has been found to be at the code level. The framework addresses this problem. Finally, a flexible approach to test sub-set extraction is presented, allowing testing resources to be deployed effectively and efficiently.