Automatic Test Data Generation for Basis Path Testing

Objectives: This paper presents a new hybrid ACO-NSA algorithm for the automatic test data generation problem with path coverage as an objective function. Method: In it, at first instance, (detectors) are generated ant colony optimization (ACO), and then set (detector set) has been refined by negative selection (NSA) Hamming distance. Findings: The algorithm’s performance is tested on several benchmark problems different types variables metrics average coverage, generations, time success rate, Iteration value 1000 200 rate. obtained results from proposed approach compared some existing approaches. very efficient high efficacy, higher minimal redundancy, less execution time. Applications: can be applied in any type of software development process engineering to reduce testing efforts. Novelty: based two distinct methodologies: metaheuristic search artificial immune search, its fitness measured using provides 99.5% 2.72% number generations 0.07 ns, 99.9% which significantly better than comparable Keywords: Test generation; Metaheuristic search; Artificial Ant optimization; Negative algorithm; Path