Multi-objective Firefly Algorithm for Test Data Generation with Surrogate Model

2021 ◽  
pp. 283-299
Author(s):  
Wenning Zhang ◽  
Qinglei Zhou ◽  
Chongyang Jiao ◽  
Ting Xu
Keyword(s):  
Test Data ◽  
Surrogate Model ◽  
Firefly Algorithm ◽  
Test Data Generation ◽  
Data Generation ◽  
Multi Objective

Test Data Generation and Selection Using Levy Flight-Based Firefly Algorithm

2021 ◽  
Vol 9 (2) ◽  
pp. 18-34
Author(s):  
Abhishek Pandey ◽  
Soumya Banerjee
Keyword(s):  
Test Data ◽  
Firefly Algorithm ◽  
Optimization Problem ◽  
Structural Testing ◽  
Software Test ◽  
Testing Time ◽  
Test Data Generation ◽  
Data Generation ◽  
Test Suite Optimization ◽  
Test Optimization

This article discusses the application of an improved version of the firefly algorithm for the test suite optimization problem. Software test optimization refers to optimizing test data generation and selection for structural testing criteria for white box testing. This will subsequently reduce the two most costly activities performed during testing: time and cost. Recently, various search-based approaches proved very interesting results for the software test optimization problem. Also, due to no free lunch theorem, scientists are continuously searching for more efficient and convergent methods for the optimization problem. In this paper, firefly algorithm is modified in a way that local search ability is improved. Levy flights are incorporated into the firefly algorithm. This modified algorithm is applied to the software test optimization problem. This is the first application of Levy-based firefly algorithm for software test optimization. Results are shown and compared with some existing metaheuristic approaches.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wenning Zhang ◽  
Chongyang Jiao ◽  
Qinglei Zhou ◽  
Yang Liu ◽  
Ting Xu
Keyword(s):  
Deep Learning ◽  
Test Data ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Optimization Method ◽  
Test Data Generation ◽  
Data Generation ◽  
Whole Space ◽  
Automatic Test Data Generation ◽  
Gender Based

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.

Constrained multi‐objective test data generation based on set evolution

IET Software ◽  
2015 ◽  
Vol 9 (4) ◽  
pp. 103-108 ◽  
Author(s):  
Xiangjuan Yao ◽  
Dunwei Gong ◽  
Gongjie Zhang
Keyword(s):  
Test Data ◽  
Objective Test ◽  
Test Data Generation ◽  
Data Generation ◽  
Multi Objective

scholarly journals Evolutionary algorithms for the multi-objective test data generation problem

2011 ◽  
Vol 42 (11) ◽  
pp. 1331-1362 ◽  
Author(s):  
Javier Ferrer ◽  
Francisco Chicano ◽  
Enrique Alba
Keyword(s):  
Evolutionary Algorithms ◽  
Test Data ◽  
Objective Test ◽  
Test Data Generation ◽  
Data Generation ◽  
Multi Objective ◽  
Generation Problem
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