Abstract
Abstract Software testing is a very important technique to design the faultless software and takes approximately 60% of resources for the software development. It is the process of executing a program or application to detect the software bugs. In software development life cycle, the testing phase takes around 60% of cost and time. Test case generation is a method to identify the test data and satisfy the software testing criteria. Test case generation is a vital concept used in software testing, that can be derived from the user requirements specification. An automatic test case technique determines automatically where the test cases or test data generates utilizing search based optimization method. In this paper, Cuckoo Search and Bee Colony Algorithm (CSBCA) method is used for optimization of test cases and generation of path convergence within minimal execution time. The performance of the proposed CSBCA was compared with the performance of existing methods such as Particle Swarm Optimization (PSO), Cuckoo Search (CS), Bee Colony Algorithm (BCA), and Firefly Algorithm (FA).
Highlights
Nowadays, software is significantly used in many applications such as home appliances, Bank process, nuclear-power-plants, automobiles, telecommunications, medical devices and so on [1, 2]
The performance of the proposed Cuckoo Search and Bee Colony Algorithm (CSBCA) was compared with the performance of existing methods such as Particle Swarm Optimization (PSO), Cuckoo Search (CS), Bee Colony Algorithm (BCA), and Firefly Algorithm (FA)
CSBCA an evolutionary meta-heuristic algorithm used to optimize the automated test cases with test data. This algorithm is used to generate the test cases which are optimized by taking an example of withdrawal operation by an ATM machine automatically
Summary
Software is significantly used in many applications such as home appliances, Bank process, nuclear-power-plants, automobiles, telecommunications, medical devices and so on [1, 2]. The software testing takes more time and cost and makes the software development process an expensive task. The development of automatic test case generation process assists the software testing engineer and saves more time. The fuzzy clustering method is utilized to decrease the testing period as well as the number of test cases. The combination of fuzzy logic and CS algorithm used for software cost prediction and it provided the accurate prediction rate [14] Another existing method of optimal software testing as well as maintenance policy is Neural Network based Model (NNM). The NNM technique calculates the time period of an optimal software testing and maintenance limit by reducing the software cost.
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