scholarly journals GREEDY DISCRETE ANT COLONY OPTIMIZATION FOR HIGH COVERAGE TEST SUITE GENERATION

2017 ◽  
pp. 741-747
Author(s):  
T. Ramasundaram ◽  
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Test Suite ◽  
High Coverage ◽  
Test Suite Generation

scholarly journals Adopting Ant Colony Optimization Algorithm for Pairwise T-Way Test Suite Generation Strategy

2021 ◽  
Vol 1962 (1) ◽  
pp. 012045
Author(s):  
Nuraminah Ramli ◽  
Rozmie Razif Othman ◽  
Rimuljo Hendradi ◽  
Shukor Sanim Mohd Fauzi ◽  
Iszaidy Ismail ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Test Suite ◽  
Ant Colony Optimization Algorithm ◽  
Test Suite Generation

scholarly journals Test Case Reduction Using Ant Colony Optimization for Object Oriented Program

2015 ◽  
Vol 5 (6) ◽  
pp. 1424
Author(s):  
Sudhir Kumar Mohapatra ◽  
Srinivas Prasad
Keyword(s):  
Ant Colony Optimization ◽  
System Development ◽  
Object Oriented ◽  
Ant Colony ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Reduction Techniques ◽  
Maintenance Stage ◽  
The Cost

Software testing is one in all the vital stages of system development. In software development, developers continually depend upon testing to reveal bugs. Within the maintenance stage test suite size grow due to integration of new functionalities. Addition of latest technique force to make new test case which increase the cost of test suite. In regression testing new test case could also be added to the test suite throughout the entire testing process. These additions of test cases produce risk of presence of redundant test cases. Because of limitation of time and resource, reduction techniques should be accustomed determine and take away. Analysis shows that a set of the test case in a suit should satisfy all the test objectives that is named as representative set. Redundant test case increase the execution price of the test suite, in spite of NP-completeness of the problem there are few sensible reduction techniques are available. During this paper the previous GA primarily based technique proposed is improved to search out cost optimum representative set using ant colony optimization.

scholarly journals Software Test Suite Minimization using Ant Colony Optimization

2019 ◽  
Vol 8 (9S) ◽  
pp. 948-952
Keyword(s):  
Ant Colony Optimization ◽  
Software Maintenance ◽  
Software Industry ◽  
Optimal Solution ◽  
Maintenance Phase ◽  
Regression Testing ◽  
Ant Colony ◽  
Test Suite ◽  
High Quality ◽  
Area Of Interest

In the fast pacing technological era, the key to a successful software industry is quick delivery of high quality software to the clients. This high quality is achieved by performing software testing on the product. The high quality product ensures stakeholder’s satisfaction which in turn spreads good word about the software industry making it a success. In this paper, we will focus on the problems faced during regression testing and how the same can be handled. Regression testing is a critical activity done during the software maintenance phase of the software development cycle. However, it has countless underlying issues like effective test case generation and prioritization, etc which need to be dealt with. These issues demand effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of Ant Colony Optimization (ACO) for test suite minimization has been an area of interest for many researchers. This paper presents an implementation of ACO for test suite minimization, showcasing how arbitrary nature of ACO helps choose an optimal solution to the problem.

scholarly journals Fault-type coverage based ant colony optimization algorithm for attaining smaller test suite

2020 ◽  
Vol 9 (3) ◽  
pp. 507
Author(s):  
Bharathi M ◽  
Sangeetha V
Keyword(s):  
Ant Colony Optimization ◽  
Execution Time ◽  
Food Source ◽  
Reduction Rate ◽  
Ant Colony ◽  
Test Suite ◽  
Test Cases ◽  
Greedy Approach ◽  
Fault Type ◽  
Test Suite Optimization

<table width="0" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="593"><p>In this paper, we proposed Fault-Type Coverage Based Ant Colony Optimization (FTCBACO) technique for test suite optimization. An algorithm starts with initialization of FTCBACO factors using test cases in test suite. Then, assign separate ant to each test case called vertex. Each ant chooses best vertices to attain food source called objective of the problem by means of updating of pheromone trails and higher probability trails. This procedure is repeated up to the ant reaches food source. In FTCBACO algorithm, minimal number of test cases with less execution time chosen by an ant to cover all faults type (objective) are taken as optimal solution. We measured the performance of FTCBACO against Greedy approach and Additional Greedy Approach in terms of fault type coverage, test suite size and execution time. However, the heuristic Greedy approach and Additional Greedy approach required more execution time and maximum test suite size to provide the best resolution for test suite optimization problem. Statistical investigations are performed to finalize the performance significance of FTCBACO with other approaches that concludes FTCBACO technique enriches the reduction rate of test suite and minimizes execution time of reducing test cases efficiently.</p></td></tr></tbody></table>

scholarly journals Optimum Test Suite using Fault-Type Coverage Based Ant Colony Optimization Algorithm

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Test Suite ◽  
Computational Time ◽  
Test Case ◽  
Test Cases ◽  
Product Lines ◽  
Fault Type ◽  
Testing Cost ◽  
Test Suite Reduction

Software Product Lines(SPLs) covers a mixture of features for testing Software Application Program(SPA). Testing cost reduction is a major metric of software testing. In combinatorial testing(CT), maximization of fault type coverage and test suite reduction plays a key role to reduce the testing cost of SPA. Metaheuristic Genetic Algorithm(GA) do not offer best outcome for test suite optimization problem due to mutation operation and required more computational time. So, Fault-Type Coverage Based Ant Colony Optimization(FTCBACO) algorithm is offered for test suite reduction in CT. FTCBACO algorithm starts with test cases in test suite and assign separate ant to each test case. Ants elect best test cases by updating of pheromone trails and selection of higher probability trails. Best test case path of ant with least time are taken as optimal solution for performing CT. Hence, FTCBACO Technique enriches reduction rate of test suite and minimizes computational time of reducing test cases efficiently for CT.

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