Software testing plays a necessary role in software development and maintenance. This activity is performed to support quality assurance. It is very common to design a number of testing suite to test their programs manually for most test engineers. To design test data manually is an expensive and labor-wasting process. Base on this reason, how to generate software test data automatically becomes a hot issue. Most researches usually use the meta-heuristic search methods like genetic algorithm or simulated annealing to gain the test data. In most circumstances, test engineers will generate the test suite first if they have a new program. When they debug or change some code to become a new one, they still design another new test suite to test it. Nearly no people will reserve the first test data and reuse it. In this research, we want to discuss whether it is useful to store the original test data.

In this thesis, meta-heuristic algorithms are developed to obtain optimum or near optimum solutions for the time-cost-resource trade-off and resource leveling problems in project scheduling. Time cost trade-off, resource leveling, single-mode resource constrained project scheduling, multi-mode resource constrained project scheduling and resource constrained time cost trade-off problems are analyzed. Genetic algorithm simulated annealing, quantum simulated annealing, memetic algorithm, variable neighborhood search, particle swarm optimization, ant colony optimization and electromagnetic scatter search meta-heuristic algorithms are implemented for time cost trade-off problems with unlimited resources. In this thesis, three new meta-heuristic algorithms are developed by embedding meta-heuristic algorithms in each other. Hybrid genetic algorithm with simulated annealing presents the best results for time cost trade-off. Resource leveling problem is analyzed by five genetic algorithm based meta-heuristic algorithms. Apart from simple genetic algorithm, four meta-heuristic algorithms obtained same schedules obtained in the literature. In addition to this, in one of the test problems the solution is improved by the four meta-heuristic algorithms. For the resource constrained scheduling problems / genetic algorithm, genetic algorithm with simulated annealing, hybrid genetic algorithm with simulated annealing and particle swarm optimization meta-heuristic algorithms are implemented. The algorithms are tested by using the project sets of Kolisch and Sprecher (1996). Genetic algorithm with simulated annealing and hybrid genetic algorithm simulated annealing algorithm obtained very successful results when compared with the previous state of the art algorithms. 120-activity multi-mode problem set is produced by using the single mode problem set of Kolisch and Sprecher (1996) for the analysis of resource constrained time cost trade-off problem. Genetic algorithm with simulated annealing presented the least total project cost.