Optimal path test data generation based on hybrid negative selection algorithm and genetic algorithm.

ABSTRACT: Path testing is the basic approach of white box testing and the main approach to solve it by discovering the particular input data of the searching space to encompass the paths in the software under test. Due to the increasing software complexity, exhaustive testing is impossible and computationally not feasible. The ultimate challenge is to generate suitable test data that maximize the coverage; many approaches have been developed by researchers to accomplish path coverage. The paper suggested a hybrid method (NSA-GA) based on Negative Selection Algorithm (NSA) and Genetic Algorithm (GA) to generate an optimal test data avoiding replication to cover all possible paths. The proposed method modifies the generation of detectors in the generation phase of NSA using GA, as well as, develops a

SUBMITTER: Mohi-Aldeen SM 

PROVIDER: S-EPMC7703959 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature