Applying the Enhanced Snow Ablation Optimizer to Generate Data Flow-Based Test Data

Software quality can be effectively ensured by software testing. The creation of test data is a key component of software testing automation. One unresolved issue is how to automatically create test data sets for the data flow coverage criterion. Search-based software testing (SBST) is a technique that employs meta-heuristic search algorithms to generate test data. In this paper, a method of automatic test data generation for data flow coverage criterion based on the enhanced snow ablation optimizer (ESAO) is proposed. First, the snow ablation optimizer (SAO) is enhanced to improve the efficiency of the algorithm through the Latin hypercube sampling (LHS) initialization strategy and warming strategy. Second, the construction of the fitness function is considered in terms of both definition node and use node. Third, the data flow-based test cases are automatically generated based on the ESAO. This method of generating test cases that cover all definition-use pairs (DUPs) improves the efficiency and coverage of test case generation, and thus improves the efficiency of software testing.