A Critical Review on Automated Test Case Generation for Conducting Combinatorial Testing Using Particle Swarm Optimization

2018 ◽  
Vol 7 (3.8) ◽  
pp. 22 ◽  
Author(s):  
Dr V. Chandra Prakash ◽  
Subhash Tatale ◽  
Vrushali Kondhalkar ◽  
Laxmi Bewoor
Keyword(s):  
Particle Swarm Optimization ◽  
Critical Review ◽  
Optimization Problem ◽  
Test Suite ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Combinatorial Testing ◽  
Swarm Optimization ◽  
Automated Test Case Generation

In software development life cycle, testing plays the significant role to verify requirement specification, analysis, design, coding and to estimate the reliability of software system. A test manager can write a set of test cases manually for the smaller software systems. However, for the extensive software system, normally the size of test suite is large, and the test suite is prone to an error committed like omissions of important test cases, duplication of some test cases and contradicting test cases etc. When test cases are generated automatically by a tool in an intelligent way, test errors can be eliminated. In addition, it is even possible to reduce the size of test suite and thereby to decrease the cost & time of software testing.It is a challenging job to reduce test suite size. When there are interacting inputs of Software under Test (SUT), combinatorial testing is highly essential to ensure higher reliability from 72 % to 91 % or even more than that. A meta-heuristic algorithm like Particle Swarm Optimization (PSO) solves optimization problem of automated combinatorial test case generation. Many authors have contributed in the field of combinatorial test case generation using PSO algorithms.We have reviewed some important research papers on automated test case generation for combinatorial testing using PSO. This paper provides a critical review of use of PSO and its variants for solving the classical optimization problem of automatic test case generation for conducting combinatorial testing.   

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.

COST-COGNIZANT COMBINATORIAL TEST CASE PRIORITIZATION

2011 ◽  
Vol 21 (06) ◽  
pp. 829-854 ◽  
Author(s):  
ZIYUAN WANG ◽  
LIN CHEN ◽  
BAOWEN XU ◽  
YAN HUANG
Keyword(s):  
Physical Meaning ◽  
Resource Constraints ◽  
Regression Testing ◽  
Experimental Results ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Combinatorial Testing

Combinatorial testing has been widely used in practice. People usually assume all test cases in combinatorial test suite will run completely. However, in many scenarios where combinatorial testing is needed, for example the regression testing, the entire combinatorial test suite is not run completely as a result of test resource constraints. To improve the efficiency of testing, combinatorial test case prioritization technique is required. For the scenario of regression testing, this paper proposes a new cost-cognizant combinatorial test case prioritization technique, which takes both combination weights and test costs into account. Here we propose a series of metrics with physical meaning, which assess the combinatorial coverage efficiency of test suite, to guide the prioritization of combinatorial test cases. And two heuristic test case prioritization algorithms, which are based on total and additional techniques respectively, are utilized in our technique. Simulation experimental results illustrate some properties and advantages of proposed technique.

Metaheuristic Techniques for Test Case Generation

2022 ◽  
pp. 1043-1058
Author(s):  
Rashmi Rekha Sahoo ◽  
Mitrabinda Ray
Keyword(s):  
Software Testing ◽  
Selection Procedure ◽  
Primary Objective ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Metaheuristic Techniques

The primary objective of software testing is to locate bugs as many as possible in software by using an optimum set of test cases. Optimum set of test cases are obtained by selection procedure which can be viewed as an optimization problem. So metaheuristic optimizing (searching) techniques have been immensely used to automate software testing task. The application of metaheuristic searching techniques in software testing is termed as Search Based Testing. Non-redundant, reliable and optimized test cases can be generated by the search based testing with less effort and time. This article presents a systematic review on several meta heuristic techniques like Genetic Algorithms, Particle Swarm optimization, Ant Colony Optimization, Bee Colony optimization, Cuckoo Searches, Tabu Searches and some modified version of these algorithms used for test case generation. The authors also provide one framework, showing the advantages, limitations and future scope or gap of these research works which will help in further research on these works.

Metaheuristic Techniques for Test Case Generation

2018 ◽  
Vol 11 (1) ◽  
pp. 158-171 ◽  
Author(s):  
Rashmi Rekha Sahoo ◽  
Mitrabinda Ray
Keyword(s):  
Software Testing ◽  
Optimization Problem ◽  
Selection Procedure ◽  
Primary Objective ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Metaheuristic Techniques

The primary objective of software testing is to locate bugs as many as possible in software by using an optimum set of test cases. Optimum set of test cases are obtained by selection procedure which can be viewed as an optimization problem. So metaheuristic optimizing (searching) techniques have been immensely used to automate software testing task. The application of metaheuristic searching techniques in software testing is termed as Search Based Testing. Non-redundant, reliable and optimized test cases can be generated by the search based testing with less effort and time. This article presents a systematic review on several meta heuristic techniques like Genetic Algorithms, Particle Swarm optimization, Ant Colony Optimization, Bee Colony optimization, Cuckoo Searches, Tabu Searches and some modified version of these algorithms used for test case generation. The authors also provide one framework, showing the advantages, limitations and future scope or gap of these research works which will help in further research on these works.

scholarly journals Model-Based Test Suite Generation Using Mutation Analysis for Fault Localization

2019 ◽  
Vol 9 (17) ◽  
pp. 3492 ◽  
Author(s):  
Choi ◽  
Lim
Keyword(s):  
Mutation Analysis ◽  
Fault Localization ◽  
Test Suite ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Generation Process ◽  
State Chart ◽  
Generation Cost ◽  
Test Suite Generation

Fault localization techniques reduce the effort required when debugging software, as revealed by previous test cases. However, many test cases are required to reduce the number of candidate fault locations. To overcome this disadvantage, various methods were proposed to reduce fault-localization costs by prioritizing test cases. However, because a sufficient number of test cases is required for prioritization, the test-case generation cost remains high. This paper proposes a test-case generation method using a state chart to reduce the number of test suites required for fault localization, minimizing the test-case generation and execution times. The test-suite generation process features two phases: fault-detection test-case generation and fault localization in the test cases. Each phase uses mutation analysis to evaluate test cases; the results are employed to improve the test cases according to the objectives of each phase, using genetic algorithms. We provide useful guidelines for application of a search-based mutational method to a state chart; we show that the proposed method improves fault-localization performance in the test-suite generation phase.

An Ontology-Based Approach to Automated Test Case Generation

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Shreya Banerjee ◽  
Narayan C. Debnath ◽  
Anirban Sarkar
Keyword(s):  
Test Case Generation ◽  
Test Case ◽  
Automated Test Case Generation ◽  
Automated Test
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