Random or Genetic Algorithm Search for Object-Oriented Test Suite Generation?

2015 ◽  
Author(s):  
Sina Shamshiri ◽  
José Miguel Rojas ◽  
Gordon Fraser ◽  
Phil McMinn
Keyword(s):  
Genetic Algorithm ◽  
Object Oriented ◽  
Test Suite ◽  
Test Suite Generation ◽  
Algorithm Search

Automated and Optimized Software Test Suite Generation Technique for Structural Testing

2016 ◽  
Vol 26 (01) ◽  
pp. 1-13 ◽  
Author(s):  
M. S. Geetha Devasena ◽  
G. Gopu ◽  
M. L. Valarmathi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Test Data ◽  
Structural Testing ◽  
Test Suite ◽  
Test Case ◽  
Hybrid Technique ◽  
Generation Process ◽  
Swarm Optimization ◽  
Test Suite Generation

Software testing consumes 50% of total software development cost. Test case design gains central importance in testing activity with respect to quality. The manual test suite generation is a time consuming and tedious task which needs automation. Unit testing is normally done in stringent time schedules by the developers or rarely by testers. In structural testing, it is not possible to check exhaustively all possible test data and the quality of test is dependent heavily on the performance of single developer or tester. Thus automation and optimization is required in generating test data to assist developer or tester with the selection of appropriate test data. A novel hybrid technique is developed to automate the test suite generation process for branch coverage criteria using evolutionary testing. The hybrid technique applies both Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to automatically generate test data. This technique improves efficiency and effectiveness of test case generation process when compared to applying Genetic Algorithm or Particle Swarm Optimization alone. The performance of proposed technique is evaluated and is observed that hybrid technique reduces the number of iterations by 47% when compared to GA and PSO applied separately and it reduces the execution time by 52% than GA and 48% than PSO.

scholarly journals Random or evolutionary search for object-oriented test suite generation?

2018 ◽  
Vol 28 (4) ◽  
pp. e1660 ◽  
Author(s):  
Sina Shamshiri ◽  
José Miguel Rojas ◽  
Luca Gazzola ◽  
Gordon Fraser ◽  
Phil McMinn ◽  
...  
Keyword(s):  
Object Oriented ◽  
Test Suite ◽  
Evolutionary Search ◽  
Test Suite Generation

scholarly journals Multiple-Searching Genetic Algorithm for Whole Test Suites

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 2011
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang ◽  
Chi-En Tsai
Keyword(s):  
Genetic Algorithm ◽  
Multicast Routing ◽  
Test Suite ◽  
Test Cases ◽  
Coverage Criteria ◽  
Mutation Score ◽  
Problem Instances ◽  
Test Suite Generation ◽  
Test Suites

A test suite is a set of test cases that evaluate the quality of software. The aim of whole test suite generation is to create test cases with the highest coverage scores possible. This study investigated the efficiency of a multiple-searching genetic algorithm (MSGA) for whole test suite generation. In previous works, the MSGA has been effectively used in multicast routing of a network system and in the generation of test cases on individual coverage criteria for small- to medium-sized programs. The performance of the algorithms varies depending on the problem instances. In this experiment were generated whole test suites for complex programs. The MSGA was expanded in the EvoSuite test generation tool and compared with the available algorithms on EvoSuite in terms of the number of test cases, the number of statements, mutation score, and coverage score. All algorithms were evaluated on 14 problem instances with different corpus to satisfy multiple coverage criteria. The problem instances were Java open-source projects. Findings demonstrate that the MSGA generated test cases reached greater coverage scores and detected a larger number of faults in the test class when compared with the others.

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