FLOWER: optimal test suite reduction as a network maximum flow

A trend in software testing is reducing the size of a test suite while preserving its overall quality. Given a test suite and a set of requirements covered by the suite, test suite reduction aims at selecting a subset of test cases that cover the same set of requirements. Even though this problem has received considerable attention, finding the smallest subset of test cases is still challenging and commonly-used approaches address this problem only with approximated solutions. When executing a single test case requires much manual effort (e.g., hours of preparation), finding the minimal subset is needed to reduce the testing costs. In this paper, we introduce a radically new approach to test suite reduction, called FLOWER, based on a search among network maximum flows. From a given test suite and the requirements covered by the suite, FLOWER forms a flow network (with specific constraints) that is then traversed to find its maximum flows. FLOWER leverages the Ford-Fulkerson method to compute maximum flows and Constraint Programming techniques to search among optimal flows. FLOWER is an exact method that computes a minimum-sized test suite, preserving the coverage of requirements. The experimental results show that FLOWER outperforms a non-optimized implementation of the Integer Linear Programming approach by 15-3000 times in terms of the time needed to find an optimal solution, and a simple greedy approach by 5-15% in terms of the size of reduced test suite.