Optimized Branch and Bound for Path-wise Test Data Generation

Ya-Wen Wang, Ying Xing, Yun-Zhan Gong, Xu-Zhou Zhang


The increasing complexity of real-world programs necessitates the automation
of software testing. As a basic problem in software testing, the automation
of path-wise test data generation is especially important, which is in essence a constraint
satisfaction problem solved by search strategies. In this paper, the search
algorithm branch and bound is introduced and optimized to tackle the problem of
path-wise test data generation. The optimized branching operation is fulfilled by a
dynamic variable ordering algorithm with a heuristic rule to break ties. The optimized
bounding operation is accomplished by analyzing the results of interval arithmetic.
In order to facilitate the search methods, the solution space is represented as state
space. Experimental results prove the effectiveness of the optimized branching and
bounding operations, and show that the proposed method outperformed some other
methods used in test data generation. The results also demonstrate that the proposed
method is applicable in engineering.


test data generation, constraint satisfaction problem, branch and bound, state space search

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DOI: https://doi.org/10.15837/ijccc.2014.4.1169

Copyright (c) 2017 Ya-Wen Wang, Ying Xing, Yun-Zhan Gong, Xu-Zhou Zhang

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