scholarly journals PWiseGen: Generating test cases for pairwise testing using genetic algorithms

2011 ◽  
Keyword(s):  
Genetic Algorithms ◽  
Test Cases ◽  
Pairwise Testing

scholarly journals Software Test Data Generation based on Path Testing using Genetic Algorithms

2020 ◽  
Vol 8 (6) ◽  
pp. 4466-4473
Keyword(s):  
Genetic Algorithms ◽  
Test Data ◽  
Iterative Algorithms ◽  
Continuous Functions ◽  
Software Test ◽  
Test Cases ◽  
Test Data Generation ◽  
Data Generation ◽  
Optimal Solutions ◽  
Automated Test

Test data generation is the task of constructing test cases for predicting the acceptability of novel or updated software. Test data could be the original test suite taken from previous run or imitation data generated afresh specifically for this purpose. The simplest way of generating test data is done randomly but such test cases may not be competent enough in detecting all defects and bugs. In contrast, test cases can also be generated automatically and this has a number of advantages over the conventional manual method. Genetic Algorithms, one of the automation techniques, are iterative algorithms and apply basic operations repeatedly in greed for optimal solutions or in this case, test data. By finding out the most error-prone path using such test cases one can reduce the software development cost and improve the testing efficiency. During the evolution process such algorithms pass on the better traits to the next generations and when applied to generations of software test data they produce test cases that are closer to optimal solutions. Most of the automated test data generators developed so far work well only for continuous functions. In this study, we have used Genetic Algorithms to develop a tool and named it TG-GA (Test Data Generation using Genetic Algorithms) that searches for test data in a discontinuous space. The goal of the work is to analyze the effectiveness of Genetic Algorithms in automated test data generation and to compare its performance over random sampling particularly for discontinuous spaces.

Software Product Line Testing Based on Feature Model Mutation

2017 ◽  
Vol 27 (05) ◽  
pp. 817-839 ◽  
Author(s):  
Johnny Maikeo Ferreira ◽  
Silvia Regina Vergilio ◽  
Marcos Quinaia
Keyword(s):  
Software Product Line ◽  
Product Line ◽  
Experimental Results ◽  
Feature Model ◽  
Test Case ◽  
Test Cases ◽  
Mutation Operators ◽  
Software Product ◽  
Pairwise Testing ◽  
Feature Management

The Feature Model (FM) is a fundamental artifact of the Software Product Line (SPL) engineering, used to represent commonalities and variabilities, and also to derive products for testing. However, the test of all features combinations (products) is not always possible in practice. Due to the growing complexity of the applications, only a subset of products is usually selected. The selection is generally based on combinatorial testing, to test features interactions. This kind of selection does not consider different classes of faults that can be present in the FM. The application of a fault-based approach, such as mutation-based testing, can increase the probability of finding faults and the confidence that the SPL products match the requirements. Considering that, this paper introduces a mutation approach to select products for the feature testing of SPLs. The approach can be used similarly to a test criterion in the generation and assessment of test cases. It includes (i) a set of mutation operators, introduced to describe typical faults associated to the feature management and to the FM; and (ii) a testing process to apply the operators. Experimental results show the applicability of the approach. The selected test case sets are capable to reveal other kind of faults, not revealed in the pairwise testing.

A Analysis and Research on the Methods of Object Oriented Software Testing

2013 ◽  
Vol 709 ◽  
pp. 616-619
Author(s):  
Jing Chen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Test Data ◽  
Object Oriented ◽  
Automatic Generation ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Path Coverage ◽  
Parameter Values

This paper proposes a genetic algorithm-based method to generate test cases. This method provides information for test case generation using state machine diagrams. Its feature is realizing automation through fewer generated test cases. In terms of automatic generation of test data based on path coverage, the goal is to build a function that can excellently assess the generated test data and guide the genetic algorithms to find the targeting parameter values.

scholarly journals PWiseHA: Application of Harmony Search Algorithm for Test Suites Generation using Pairwise Techniques

2020 ◽  
Vol 9 (4) ◽  
Author(s):  
Aminu Aminu Muazu ◽  
Umar Danjuma Maiwada
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Research Paper ◽  
Harmony Search Algorithm ◽  
Test Suite ◽  
Minimum Size ◽  
Test Cases ◽  
Generation Process ◽  
Final Test ◽  
Pairwise Testing

Pairwise testing is an approach that tests every possible combinations of values of parameters. In this approach, number of all combinations are selected to ensure all possible pairs of parameter values are included in the final test suite. Generating test cases is the most active research area in pairwise testing, but the generation process of the efficient test suite with minimum size can be considered as one of optimization problem. In this research paper we articulate the problem of finding a pairwise final test suite as a search problem and the application of harmony search algorithm to solve it. Also, in this research paper, we developed a pairwise software testing tool called PWiseHA that will generate test cases using harmony search algorithm and this PWiseHA is well optimized. Finally, the result obtained from PWiseHA shows a competitive results if matched with the result of existing pairwise testing tools. PWiseHA is still in prototype form, an obvious starting point for future work.

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