Optimal Individual Selection Algorithm Based on Layer Proximity and Branch Distance Functions

Automatic generation of test cases using heuristic methods is a hot research topic nowadays. Although its advantages are obvious, it is slightly insufficient in the selection of optimal individuals. Aiming at the existing problems in the evaluation and selection of the optimal individual, this paper proposes a test case evaluation algorithm based on the comprehensive analysis of the characteristics of layer proximity and branch distance function, which is a joint structure of “layer proximity and branch distance function”. The basic idea of this algorithm is that when selecting pilot individuals in the evolutionary process, we first select the individuals with high proximity between the actual execution path and the target path, and then select the individuals with the smallest branching distances among these individuals, so as to obtain the individuals with the optimal piloting ability. Experiments show that the proposed algorithm can quickly find the optimal test cases, especially for the test case generation of multi-layer nested programs.