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.
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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