*** 9661393 McGraw This Small Business Innovative Research Phase I project is to do Dynamic test data generation to reduce test generation problems to a simpler problem of function minimization and explore a promising approach to the problem of finding test inputs that satisfy complex constraints. Genetic algorithms to perform this function minimization will be used in place of the simple gradient-descent method that is currently used. Also, the application of dynamic software analysis to the problem of deciding which program execution path will allow the constraints to be satisfied most easily will be investigated. In software testing, it is often desirable to find test inputs that exercise specific program features. To find these inputs by hand is extremely time-consuming, especially when the software is complex. Therefore, numerous attempts have been made to automate the process. Random test data generation consists of generating test inputs at random. But when the desired inputs must satisfy complex constraints, a random approach is unlikely to succeed. Symbolic test data generation executes parts of the software symbolically, constructing an algebraic description of the constraints that an input must satisfy in order to exercise the desired feature. But this method falters when certain programming constructs are encountered. The potential benefactors of the research include the following industries: aviation, defense, nuclear, telecommunications, medical, pharmaceutical, transportation, and personal computer software. ***
