Combinatorial testing has become an important approach in software testing, and many algorithms have been developed for generating combinatorial test cases. However, few have considered the constraints between the input parameters. In this paper, we summarize existing combinatorial testing strategies with constraints and propose two novel algorithms, namely, the CCS (construct constraint set) algorithm and the CTWC (combinatorial testing with constraints) algorithm. We first utilize CCS to compute implied constraints, and then facilitate CTWC to generate test cases for the system under test.We evaluate the CCS algorithm using a case study and compare the results of the CTWC algorithm with some existing strategies.Experimental results show that our algorithm outperformed them in terms of the number of test cases.

Mass attack by the pine shoot beetle. Toniiciispiizipel'uil (L,), on the trunks of libiiig trees was a main cause of Yunnan pine forest damages in Kunming, China, from the 1970s to 1990s. The present study, based on analysis of egg galleries. proposed that mass attack was initiated with the establishment of a few primary invaders and intensified as the partners followed. The attacking beetles increased with time in the first part of a mass attack. Dispersion of the population on the trunk surface was started from the positions the primary invaders colonized and spread to nearby locations. The population was mainly distributed in the upper and middle of trunk, but revealed the highest density in the middle.

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Attribute reduction is one of the key issues in rough set theory. Many heuristic reduction strategies such as forward heuristic reduction, backward heuristic reduction and for-backward heuristic reduction have been proposed to obtain a subset of attributes which has the same discernibility as the original attribute set. However, some methods are usually computationally time consuming for large data sets. Therefore, this paper focuses on solving the attribute reduction efficiency in the decision system. We first introduce the quotient of approximation, positive region and conflict region, and then research the heuristic reduction algorithm based on conflict region. Sequentially, we put forward to a mechanism of bidirectional heuristic attribute reduction based on conflict region quotient and design a bidirectional heuristic attribute reduction algorithm. Finally, the experimental results with UCI data sets show that the proposed reduction algorithm is an effective technique to deal with large high-dimensional data sets.

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Pairwise testing has become an important approach to software testing because it often provides effective error detection at low cost, and a key problem of it is the test case generation method. As the part of an effort to develop an optimized strategy for pairwise testing, this paper proposes an efficient pairwise test case generation strategy, called VIPO(Variant of In-Parameter-order ), which is a variant of IPO strategy. We compare its effectiveness with some existing strategies including IPO, Tconfig, Pict and AllPairs. Experimental results demonstrate that VIPO outperformed them in terms of the number of generated test case within reasonable execution times, in most cases.

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Attribute reduction is one of key issues in rough set theory, and positive region reduct is a classical type of reducts. However, a lot of reduction algorithms have more high time expenses when dealing with high-volume and high-dimensional data sets. To overcome this shortcoming, in this paper, a relative discernibility reduction method based on the simplified decision table of the original decision table is researched for obtaining positive region reduct. Moreover, to further improve performance of reduction algorithm, we develop an accelerator for attribute reduction, which reduces the radix sort times of the reduction process to raise algorithm efficiency. By the accelerator, two positive region reduction algorithms, i.e., FARA-RS and BARA-RS, based on the relative discernibility are designed. FARA-RS simultaneously reduce the size of the universe and the number of radix sort to achieve speedup and BARA-RS only reduce the number of radix sort to achieve acceleration. The experimental results show that the proposed reduction algorithms are effective and feasible for high dimensional and large data sets.