Abstract
A novel approach to automating test data generation for distributed programs is presented. The approach is based on actual execution of the program under test, a run-time scheduler, function minimization methods, and dynamic dataflow analysis. Test data are developed for the program using actual values of the input variables. When the program is executed, the program execution flow is monitored. If during program execution an undesirable execution flow is observed then function minimization search algorithms are used to automatically locate the values of input variables for which the selected path is traversed. In addition, dynamic dataflow analysis is used to determine those input variables responsible for the undesirable program behavior, which can lead to significant speed-up of the search process. >
Published Version
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