Identification of potentially infeasible program paths by monitoring the search for test data

Abstract

A tool and techniques are presented for test data generation and identification of a path's likely unfeasibility in structural software testing. The tool is based on the dynamic technique and search using genetic algorithms. Our work introduces a new fitness function that combines control and data flow dynamic information to improve the process of search for test data. The unfeasibility issue is addressed by monitoring the genetic algorithm's search progress. An experiment shows the validity of the developed solutions and the benefit of using the tool.