RGA: A lightweight and effective regeneration genetic algorithm for coverage-oriented software test data generation

Yang, Shunkun; Man, Tianlong; Xu, Jiaqi; Zeng, Fuping; Li, Ké

doi:10.1016/j.infsof.2016.04.013

Cited by 20 publications

(21 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The suggested improvements involved presenting a new technique to order the program code branches, for being selected as the coverage goals, transferring a maximum of 10 percent of the desired elements of each generation to the next generation, and storing several generated population's elements for a branch into a generated initial population of the sibling branch. Yang et al [16] proposed a new search-based method for test data generation, called the regenerate genetic algorithm (RGA). RGA adds a new regeneration strategy to the traditional GA, which enables it to avoid the possible local stagnation.…”

Section: Search-based Methods For Test Data Generation In Sequential mentioning

confidence: 99%

A Search-Based Test Data Generation Method for Concurrent Programs

Mirhosseini¹,

Haghighi²

2020

IJCIS

View full text Add to dashboard Cite

Concurrent programs are being widely adopted in development of multi-core and many-core processors. However, these types of programs present some features such as concurrency, communication and synchronization which make their testing more challenging than sequential programs. Search-based techniques, which use meta-heuristic search algorithms, have frequently been used for testing sequential programs, especially in the test data generation activity. However, application of search-based techniques in test data generation for concurrent programs has seldom been covered in the literature. The first contribution of this paper is to present a search-based test data generation framework for concurrent programs. Additionally, a hybrid meta-heuristic algorithm, called SFLA-VND, is proposed, which could be used in the mentioned framework as well as other meta-heuristic algorithms. SFLA-VND is a combination of the shuffled frog leaping algorithm (SFLA) and the variable neighborhood descent (VND). The proposed framework has been experimented on five concurrent benchmark programs by applying genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), SFLA and SFLA-VND. Experimental results demonstrate the effectiveness and efficiency of this framework. Also, the results confirm the superiority of SFLA-VND in comparison with some popular meta-heuristic algorithms, when they are used for test data generation.

show abstract

Section: Search-based Methods For Test Data Generation In Sequential mentioning

confidence: 99%

A Search-Based Test Data Generation Method for Concurrent Programs

Mirhosseini¹,

Haghighi²

2020

IJCIS

View full text Add to dashboard Cite

show abstract

“…For example, applying a GA for test case generation corresponds with du-path coverage to execute all paths where variables are defined and used [8]. GAs have been applied to evaluate test cases to satisfy mutation testing [9] and used to generate test cases in accordance with branch coverage [10][11][12][13].…”

Section: Motivation and Related Workmentioning

confidence: 99%

“…GAs have been used by researchers to generate test cases during software development [12,[17][18][19]. GAs utilize chromosomes that represent test cases or a set of test data.…”

Section: Test Case Generation Using Gasmentioning

confidence: 99%

Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases

2020

View full text Add to dashboard Cite

Software testing using traditional genetic algorithms (GAs) minimizes the required number of test cases and reduces the execution time. Currently, GAs are adapted to enhance performance when finding optimal solutions. The multiple-searching genetic algorithm (MSGA) has improved upon current GAs and is used to find the optimal multicast routing in network systems. This paper presents an analysis of the optimization of test case generations using the MSGA by defining suitable values of MSGA parameters, including population size, crossover operator, and mutation operator. Moreover, in this study, we compare the performance of the MSGA with a traditional GA and hybrid GA (HGA). The experimental results demonstrate that MSGA reaches the maximum executed branch statements in the lowest execution time and the smallest number of test cases compared to the GA and HGA.

show abstract

“…Experiment results show that suggested approach performs better than simple path coverage. Yang et al [5] used a modified GA called regenerate genetic algorithm (RGA), which solves the population aging problem of the basic genetic algorithm. This algorithm regenerates population when population aging crosses the threshold limit.…”

Section: A Ga In Structural Testingmentioning

confidence: 99%

Research on use of Nature Inspired Algorithms in Software Testing

Sharma¹,

Rizvi²,

Sharma³

2019

IJITEE

View full text Add to dashboard Cite

The intention behind software testing is discovering defects in the developed software, making it error-free, robust and trustworthy. A large quantum of total efforts in the development of software is done on software testing. These efforts may include time, cost and most importantly manpower. The number of efforts required in software testing may be reduced if test data is generated automatically, without trade off the quality of the developed software. In literature, various nature inspired algorithms are used for the optimization of the software testing process. This article is a brief study on the applications of nature inspired algorithms in software testing. To keep the study succinct this study considers only two widely used testing types, structural and functional testing and literature available, since 2010 by considering only SCI or SCOPUS indexed publications.

show abstract

RGA: A lightweight and effective regeneration genetic algorithm for coverage-oriented software test data generation

Cited by 20 publications

References 16 publications

A Search-Based Test Data Generation Method for Concurrent Programs

A Search-Based Test Data Generation Method for Concurrent Programs

Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases

Research on use of Nature Inspired Algorithms in Software Testing

Contact Info

Product

Resources

About