path-wise test data generation based on heuristic look-ahead methods

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ID: 149024
2014
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Abstract
Path-wise test data generation is generally considered an important problem in the automation of software testing. In essence, it is a constraint optimization problem, which is often solved by search methods such as backtracking algorithms. In this paper, the backtracking algorithm branch and bound and state space search in artificial intelligence are introduced to tackle the problem of path-wise test data generation. The former is utilized to explore the space of potential solutions and the latter is adopted to construct the search tree dynamically. Heuristics are employed in the look-ahead stage of the search. Dynamic variable ordering is presented with a heuristic rule to break ties, values of a variable are determined by the monotonicity analysis on branching conditions, and maintaining path consistency is achieved through analysis on the result of interval arithmetic. An optimization method is also proposed to reduce the search space. The results of empirical experiments show that the search is conducted in a basically backtrack-free manner, which ensures both test data generation with promising performance and its excellence over some currently existing static and dynamic methods in terms of coverage. The results also demonstrate that the proposed method is applicable in engineering.
Reference Key
xing2014mathematicalpath-wise Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Ying Xing;Yun-Zhan Gong;Ya-Wen Wang;Xu-Zhou Zhang
Journal journal of power sources
Year 2014
DOI
10.1155/2014/642630
URL
http://dx.doi.org/10.1155/2014/642630
Keywords
civil engineering (general)mathematics engineering (general)

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