"Application of ACO and GA Principles for Solving the Constrained Path-based Testing Problem"

"Dataset for scientific paper evaluating algorithms solving recently consolidated Constrained path-based Testing (CPT) problem. CPT is a fundamental tool in generating test paths from directed-graph-based System Under Test (SUT) models with constraints, positioned in between simple test paths generation and more complex models, as Extended Finite State Machines. The paper presents two new agorithms to solve the CPT problem, one inspired by the Ant Colony Optimization principle, denoted CPA, and the second one based on Genetic Algorithms, denoted CPG. Pseudocodes of these algorithms are presented and the quality of solution produced by these algorithms are compared with two previously published algorithms that promised offering the best solution so far. These comparisons are made using 300 CPT problem instances, most of which are derived from real projects and codebases. In the dataset we include the JSON with the source of the instances and CSV of its properties, CSV files with the detailed results of the algorithms executions, and Python source files used for the statistical evaluation of the results and the generation of the plots."