A Hybrid Genetic Algorithm and Evolutionary Strategy to Automatically Generate Test Data for Dynamic, White-Box Testing

Software testing is an important and time consuming part of the software development cycle. While automated testing frameworks do help in reducing the amount of programmer time that testing requires, the onus is still upon the programmer to provide such a framework with the inputs upon which the software must be tested. This requires static analysis of the source code, which is more effective when performed as a peer review exercise and is highly dependent on the skills of the programmers performing the analysis. It also demands the allocation of precious time for those very highly skilled programmers. An algorithm that automatically generates inputs to satisfy test coverage criteria for the software being tested would therefore be quite valuable, as it would imply that the programmer no longer needs to analyze code to generate the relevant test cases. This thesis explores a hybrid evolutionary strategy with an evolutionary algorithm to discover such test cases , in an improvement over previous methods which overly focus their search without maintaining the diversity required to cover the entire search space efficiently