Test case generation for path coverage. (c2000)

Abstract

Simulated Annealing (SA) and Genetic Algorithms (GA) have been proposed for generating integer-value test cases for path coverage. In this work, we suggest significant improvements to these algorithms and present empirical results that show their capabilities. The improvements cover algorithmic and implementation issues. They also add the capability of generating real-value subject programs for path coverage. We empirically compare the SA and GA algorithms with a hill-climbing algorithm, Korel's algorithm, for integer-value subject programs and compare SA and GA with each other on real-value subject programs. Our empirical work uses eight subject programs and a total of 49 paths. The results show that: (a) SA and GA are superior to Korel's algorithm in the number of covered paths, (b) SA tends to perform slightly better than GA in terms of the number of covered paths, and (c) GA is faster than SA; however, Korel, when it succeeds in finding the solution, is the fastest.