Gender-Based Deep Learning Firefly Optimization Method for Test Data Generation

Software testing is a widespread validation means of software quality assurance in industry. Intelligent optimization algorithms have been proved to be an effective way of automatic test data generation. Firefly algorithm has received extensive attention and been widely used to solve optimization problems because of less parameters and simple implement. To overcome slow convergence rate and low accuracy of the firefly algorithm, a novel firefly algorithm with deep learning is proposed to generate structural test data. Initially, the population is divided into male subgroup and female subgroup. Following the randomly attracted model, each male firefly will be attracted by another randomly selected female firefly to focus on global search in whole space. Each female firefly implements local search under the leadership of the general center firefly, constructed based on historical experience with deep learning. At the final period of searching, chaos search is conducted near the best firefly to improve search accuracy. Simulation results show that the proposed algorithm can achieve better performance in terms of success coverage rate, coverage time, and diversity of solutions.

Automatic Test Data Generation Using Bio-Inspired Algorithms

This chapter presents an overview of some widely accepted bio-inspired metaheuristic algorithms which would be helpful in solving the problems of software testing. Testing is an integral part of the software development process. A sizable number of Nature based algorithms coming under the per- view of metaheuristics have been used by researchers to solve practical problems of different disciplines of engineering and computer science, and software engineering. Here an exhaustive review of metaheuristic algorithms which have been employed to optimize the solution of test data generation for past 20 -30 years is presented. In addition to this, authors have reviewed their own work has been developed particularly to generate test data for path coverage based testing using Cuckoo Search and Gravitational Search algorithms. Also, an extensive comparison with the results obtained using Genetic Algorithms, Particle swarm optimization, Differential Evolution and Artificial Bee Colony algorithm are presented to establish the significance of the study.

A Sound Assessment of Test Suite Minimization Techniques

During software development, testing and re-testing occurs frequently to ensure that the software is working correctly before and after modifications. To carry out an effective testing process a test suite is created and executed to detect the faults in the existing code as well as in the modified code. The manual approach of test suite creation and execution is time consuming and labour intensive task as compared to automatically generated test data or test suite. The automatic test data generation is supposed to be an effective way, but a lot of redundant test cases are generated that increase the time, effort and cost of testing. Therefore, test suite minimization techniques are used to further minimize or reduce the number of test cases by selecting a subset from an initially random and large test suite to test the code before as well as after modification. In this study, a comprehensive analysis of the different test suite minimization techniques is presented in order to extend the existing studies and to propose new ideas in this direction.

Automatic Test Data Generation Using Bio-Inspired Algorithms

This chapter presents an overview of some widely accepted bio-inspired metaheuristic algorithms which would be helpful in solving the problems of software testing. Testing is an integral part of the software development process. A sizable number of Nature based algorithms coming under the per- view of metaheuristics have been used by researchers to solve practical problems of different disciplines of engineering and computer science, and software engineering. Here an exhaustive review of metaheuristic algorithms which have been employed to optimize the solution of test data generation for past 20 -30 years is presented. In addition to this, authors have reviewed their own work has been developed particularly to generate test data for path coverage based testing using Cuckoo Search and Gravitational Search algorithms. Also, an extensive comparison with the results obtained using Genetic Algorithms, Particle swarm optimization, Differential Evolution and Artificial Bee Colony algorithm are presented to establish the significance of the study.

Nature inspired algorithm using particle swarm approach with variations in inertia weights for automatic test data generation based on dominance concepts

In software testing, testing of all program statements is a very crucial issue as it consumes a lot of time, effort and cost. The time, effort and cost can be reduced by using an efficient technique to reduce the test case and a good optimization algorithm to generate efficient, reliable and unique test cases. In this paper, the concept of dominance tree is used which covers all edges/statement by using minimum test case. Nature inspired algorithm - PSO (Particle Swarm Optimization) by applying different inertia weights is used to generate unique, reliable and efficient test cases to cover the leaf nodes of dominance tree. Inertia weights like fixed inertia weight (FIW), global-local best (GLbestIW), Time-Dependent weight (TDW), and proposed GLbestRandIW weights are used with PSO to investigate the effect of inertia weights on the execution of PSO with respect to number of generation required, percentage coverage , total test cases generated to test the software under consideration.