摘要
The In-Parameter-Order （IPO） algorithm is a widely used strategy for the construction of software test suites for combinatorial testing （CT） whose goal is to reveal faults triggered by interactions among parameters. Variants of IPO have been shown to produce test suites within reasonable amounts of time that are often not much larger than the smallest test suites known. When an entire test suite is executed, all faults that arise from t-way interactions for some fixed t are surely found. However, when tests are executed one at a time, it is desirable to detect a fault as early as possible so that it can be repaired. The basic IPO strategies of horizontal and vertical growth address test suite size, but not the early detection of faults. In this paper, the growth strategies in IPO are modified to attempt to evenly distribute the values of each parameter across the tests. Together with a reordering strategy that we add, this modification to IPO improves the rate of fault detection dramatically （improved by 31% on average）. Moreover, our modifications always reduce generation time （2 times faster on average） and in some eases also reduce test suite size.
The In-Parameter-Order （IPO） algorithm is a widely used strategy for the construction of software test suites for combinatorial testing （CT） whose goal is to reveal faults triggered by interactions among parameters. Variants of IPO have been shown to produce test suites within reasonable amounts of time that are often not much larger than the smallest test suites known. When an entire test suite is executed, all faults that arise from t-way interactions for some fixed t are surely found. However, when tests are executed one at a time, it is desirable to detect a fault as early as possible so that it can be repaired. The basic IPO strategies of horizontal and vertical growth address test suite size, but not the early detection of faults. In this paper, the growth strategies in IPO are modified to attempt to evenly distribute the values of each parameter across the tests. Together with a reordering strategy that we add, this modification to IPO improves the rate of fault detection dramatically （improved by 31% on average）. Moreover, our modifications always reduce generation time （2 times faster on average） and in some eases also reduce test suite size.
基金
the National Natural Science Foundation of China under Grant Nos. 61300007 and 61305054, the Fundamental Research Funds for the Central Universities of China under Grant Nos. YWF-15-GJSYS-106 and YWF-14-JSJXY-007, and the Project of the State Key Laboratory of Software Development Environment of China under Grant Nos. SKLSDE-2015ZX-09 and SKLSDE-2014ZX-06.
