Porantim-Opt: Optimizing the Combined Selection of Model-Based Testing Techniques

Abstract

The combination of testing techniques is considered an effective strategy to evaluate a software product. However, the selection of which techniques to combine in a software project has been an interesting challenge in the Software Engineering field. This paper presents a proposal extending an approach developed to support the combined selection of model-based testing (MBT) techniques, named Porantim, applying Multiobjective Combinatorial Optimization strategy by determining the smallest dominating set in a bipartite and weighted graph. Thus, a local search strategy algorithm is proposed generating solutions aiming at maximizing the coverage of software project characteristics and skills required by the testing team to use the techniques and minimizing the eventual effort to construct models used for test cases generation. A preliminary evaluation analyzes this new approach when compared to the Porantim's original version, and the results indicate improvements in the MBT techniques selection.