A genetic algorithm based approach for prioritization of test case scenarios in static testing

White box testing is a test technique that takes into account program code, code structure and internal design flow. White box testing is primarily of two kinds-static and structural. Whereas static testing requires only the source code of the product, not the binaries or executables, in structural testing tests are actually run by the computer on built products. In this paper, we propose a technique for optimizing static testing efficiency by identifying the critical path clusters using genetic algorithm. The testing efficiency is optimized by applying the genetic algorithm on the test data. The test case scenarios are derived from the source code. The information flow metric is adopted in this work for calculating the information flow complexity associated with each node of the control flow graph generated from the source code. This research paper is an extension of our previous research paper [18].