Test-data generation directed by program path coverage through imperialist competitive algorithm

2019 ◽  
Vol 184 ◽  
pp. 102304 ◽  
Author(s):  
Mohammad Ali Saadatjoo ◽  
Seyed Morteza Babamir
Keyword(s):  
Test Data ◽  
Imperialist Competitive Algorithm ◽  
Test Data Generation ◽  
Data Generation ◽  
Competitive Algorithm ◽  
Path Coverage

Test Data Generation for Path Coverage of MPI Programs Using SAEO

2021 ◽  
Vol 30 (2) ◽  
pp. 1-37
Author(s):  
Dunwei Gong ◽  
Baicai Sun ◽  
Xiangjuan Yao ◽  
Tian Tian
Keyword(s):  
Test Data ◽  
Test Data Generation ◽  
Data Generation ◽  
Path Coverage

Automatic Test Data Generation Using Bio-Inspired Algorithms

2018 ◽  
pp. 140-159 ◽  
Author(s):  
Madhumita Panda ◽  
Sujata Dash
Keyword(s):  
Test Data ◽  
Artificial Bee Colony Algorithm ◽  
Cuckoo Search ◽  
Metaheuristic Algorithms ◽  
Test Data Generation ◽  
Data Generation ◽  
Bee Colony ◽  
Path Coverage ◽  
Automatic Test Data Generation ◽  
Generate Test Data

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.

Test Data Generation Based on Test Path Discovery Using Intelligent Water Drop

2012 ◽  
Vol 3 (2) ◽  
pp. 56-74 ◽  
Author(s):  
Praveen Ranjan Srivastava ◽  
Amitkumar Patel ◽  
Kunal Patel ◽  
Prateek Vijaywargiya
Keyword(s):  
Test Data ◽  
Water Drop ◽  
Automatic Generation ◽  
Control Flow ◽  
Test Cases ◽  
Test Data Generation ◽  
Data Generation ◽  
Path Coverage ◽  
Automatic Test Data Generation ◽  
Generate Test Data

Automatic test data generation is required to generate test cases dynamically for a specific software program. Manual generation of test data is too tedious and a time consuming task. This paper proposes a technique using Intelligent Water Drop (IWD) for automatic generation of test data. Correctly generated test data helps in reducing the effort while testing the software. This paper discusses different algorithms based on IWD to generate test data and path coverage over Control Flow Graph. Test data is generated keeping in mind all of the programming constraints like “if,” “while,” “do while,” etc., available in the program.

Test data generation approach for basis path coverage

2012 ◽  
Vol 37 (3) ◽  
pp. 1-7 ◽  
Author(s):  
Shujuan Jiang ◽  
Yanmei Zhang ◽  
Dandan Yi
Keyword(s):  
Test Data ◽  
Test Data Generation ◽  
Data Generation ◽  
Path Coverage

scholarly journals Evolutionary algorithms for path coverage test data generation and optimization: a review

2019 ◽  
Vol 15 (1) ◽  
pp. 504
Author(s):  
Deepti Bala Mishra ◽  
Arup Abhinna Acharya ◽  
Rajashree Mishra
Keyword(s):  
Evolutionary Algorithms ◽  
Test Data ◽  
Automatic Testing ◽  
Test Case ◽  
Test Data Generation ◽  
Data Generation ◽  
Branch Coverage ◽  
Path Testing ◽  
Path Coverage ◽  
Manual Testing

Software testing is very time consuming, labor-intensive and complex process. It is found that 50% of the resources of the software development are consumed for testing. Testing can be done in two different ways such as manual testing and automatic testing. Automatic testing can overcomes the limitations of manual testing by decreasing the cost and time of testing process. Path testing is the strongest coverage criteria among all white box testing techniques as it can detect about 65% of defects present in a SUT. With the help of path testing, the test cases are created and executed for all possible paths which results in 100% statement coverage and 100% branch coverage .This paper presents a systematic review of test data generation and optimization for path testing using Evolutionary Algorithms (EAs). Different EAs like GA, PSO, ACO, and ABCO based methods has been already proposed for automatic test case generation and optimization to achieve maximum path coverage.

scholarly journals Optimal path test data generation based on hybrid negative selection algorithm and genetic algorithm

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242812
Author(s):  
Shayma Mustafa Mohi-Aldeen ◽  
Radziah Mohamad ◽  
Safaai Deris
Keyword(s):  
Genetic Algorithm ◽  
Test Data ◽  
Hybrid Method ◽  
Negative Selection ◽  
Test Data Generation ◽  
Data Generation ◽  
Selection Algorithm ◽  
Data Types ◽  
Negative Selection Algorithm ◽  
Path Coverage

Path testing is the basic approach of white box testing and the main approach to solve it by discovering the particular input data of the searching space to encompass the paths in the software under test. Due to the increasing software complexity, exhaustive testing is impossible and computationally not feasible. The ultimate challenge is to generate suitable test data that maximize the coverage; many approaches have been developed by researchers to accomplish path coverage. The paper suggested a hybrid method (NSA-GA) based on Negative Selection Algorithm (NSA) and Genetic Algorithm (GA) to generate an optimal test data avoiding replication to cover all possible paths. The proposed method modifies the generation of detectors in the generation phase of NSA using GA, as well as, develops a fitness function based on the paths’ prioritization. Different benchmark programs with different data types have been used. The results show that the hybrid method improved the coverage percentage of the programs’ paths, even for complicated paths and its ability to minimize the generated number of test data and enhance the efficiency even with the increased input range of different data types used. This method improves the effectiveness and efficiency of test data generation and maximizes search space area, increasing percentage of path coverage while preventing redundant data.

Automatic Test Data Generation Using Bio-Inspired Algorithms

2021 ◽  
pp. 848-866
Author(s):  
Madhumita Panda ◽  
Sujata Dash
Keyword(s):  
Test Data ◽  
Artificial Bee Colony Algorithm ◽  
Cuckoo Search ◽  
Metaheuristic Algorithms ◽  
Test Data Generation ◽  
Data Generation ◽  
Bee Colony ◽  
Path Coverage ◽  
Automatic Test Data Generation ◽  
Generate Test Data

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.

scholarly journals Generating Test Data for Software Structural Testing using Particle Swarm Optimization

2017 ◽  
Author(s):  
Dinh Thi
Keyword(s):  
Particle Swarm Optimization ◽  
Test Data ◽  
Program Analysis ◽  
Particle Swarm ◽  
Structural Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
Swarm Optimization ◽  
Data Generator ◽  
Path Coverage

Search-based test data generation is a very popular domain in the field of automatic test data generation. However, existing search-based test data generators suffer from some problems. By combining static program analysis and search-based testing, our proposed approach overcomes one of these problems. Considering the automatic ability and the path coverage as the test adequacy criterion, this paper proposes using Particle Swarm Optimization, an alternative search technique, for automating the generation of test data for evolutionary structural testing.  Experimental results demonstrate that our test data generator can generate suitable test data has higher path coverage than the previous one.

Sign in / Sign up

Export Citation Format

Share Document