Optimize SPL test cases with adaptive simulated annealing genetic algorithm

In Software Product Line (SPL) testing, reduced test suite with high coverage is useful for early features interaction detection. sGA (simplified genetic algorithm) and SAGA(simulated annealing genetic algorithm) can generate high coverage test suite. However, small probability mutations in updating test suite may reduce search efficiency and thus miss better solutions. An improved test cases generation method based on ASAGA (Adaptive simulated annealing genetic algorithm) is proposed. Experiments on SPLOT (Software Product Lines Online Tools) feature models show that the proposed hybrid ASAGA method can ensure local optimization accuracy and achieve smaller-size test suite with higher coverage.