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.

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.

Optimal Test Case Selection Using Ant Colony and Rough Sets

2020 ◽  
Vol 11 (2) ◽  
pp. 1-14
Author(s):  
Angelin Gladston ◽  
Niranjana Devi N.
Keyword(s):  
Optimization Technique ◽  
Computational Effort ◽  
Ant Colony ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Optimal Test ◽  
Average Percentage ◽  
Test Case Selection ◽  
Case Selection

Test case selection helps in improving quality of test suites by removing ambiguous, redundant test cases, thereby reducing the cost of software testing. Various works carried out have chosen test cases based on single parameter and optimized the test cases using single objective employing single strategies. In this article, a parameter selection technique is combined with an optimization technique for optimizing the selection of test cases. A two-step approach has been employed. In first step, the fuzzy entropy-based filtration is used for test case fitness evaluation and selection. In second step, the improvised ant colony optimization is employed to select test cases from the previously reduced test suite. The experimental evaluation using coverage parameters namely, average percentage statement coverage and average percentage decision coverage along with suite size reduction, demonstrate that by using this proposed approach, test suite size can be reduced, reducing further the computational effort incurred.

Study of Test Cases Prioritization Based on Ant Colony Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 2168-2172
Author(s):  
Lu Lu Chen ◽  
Ling Zhang
Keyword(s):  
Ant Colony Algorithm ◽  
Regression Testing ◽  
Time Limit ◽  
Ant Colony ◽  
Test Suite ◽  
Test Cases ◽  
Optimal Sequence ◽  
Effectiveness And Efficiency ◽  
Important Activity

Regression testing is an important activity to ensure the quality of software. In order to improve the efficiency of regression testing, in this paper, the author proposes to reorder test suite based on ant colony algorithm in regression testing, and compare the result with other common sort results. Through experiment, it shows that the method can get the optimal sequence of test cases under the time limit and it has been proven a superior method in both effectiveness and efficiency for test cases prioritization.

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.

A Hybrid Technique for Selection and Minimization of Test Cases in Regression Testing

2019 ◽  
Vol 12 ◽  
Author(s):  
Leena Singh ◽  
Shailendra Narayan Singh ◽  
Sudhir Dawra
Keyword(s):  
Execution Time ◽  
Firefly Algorithm ◽  
Hybrid Approach ◽  
Regression Testing ◽  
Test Suite ◽  
Test Cases ◽  
Hybrid Technique ◽  
Time Saving ◽  
Superior Result ◽  
Software Practitioners

Background: In today’s era, modifications in a software is a common requirement by customers. When changes are made to existing software, re-testing of all the test cases is required to ensure that the newly introduced changes do not have any unwanted effect on the behavior of the software. However, re-testing of all the test cases would not only be time consuming but also expensive. Therefore, there is a need for a technique that reduces the number of tests to be performed. Regression testing is one of the ways to reduce the number of test cases. Selection technique is one such method which seeks to identify the test cases that are relevant to some set of recent changes. Objective: It is evident that most of the studies have used different selection techniques and have focused only on one parameter for achieving reduced test suite size without compromising the performance of regression testing. However, to the best of our knowledge, no study has taken two or more parameters of coverage, and/or execution time in a single testing. This paper presents a hybrid technique that combines both regression test selection using slicing technique and minimization of test cases using modified firefly algorithm with combination of parameters coverage and execution time in a single testing. Methods: A hybrid technique has been described that combines both selection and minimization. Selection of test cases is based upon slicing technique while minimization is done using firefly algorithm. Hybrid technique selects and minimizes the test suite using information on statement coverage and execution time. Results: The proposed technique gives 43.33% much superior result as compared to the other hybrid approach in terms of significantly reduced number of test cases. It shows that the resultant test cases were effective enough to cover 100% of the statements, for all the programs. The proposed technique was also tested on four different programs namely Quadratic, Triangle, Next day, Commission respectively for test suite selection and minimization which gave comparatively superior result in terms of reduction (%) in number of test cases required for testing. Conclusion: The combination of parameters used in slicing based approach, reduces the number of test cases making software testing an economical, feasible and time saving option without any fault in the source code. This proposed technique can be used by software practitioners/experts to reduce time, efforts and resources for selection and minimization of test cases.

An Adapted Ant-Inspired Algorithm for Enhancing Web Service Composition

Web Services ◽  
2019 ◽  
pp. 904-921
Author(s):  
Fadl Dahan ◽  
Khalil El Hindi ◽  
Ahmed Ghoneim
Keyword(s):  
Quality Of Service ◽  
Web Service ◽  
Ant Colony Optimization ◽  
Service Composition ◽  
Execution Time ◽  
Web Service Composition ◽  
Ant Colony ◽  
Diverse Population ◽  
Flexible Framework

Web Service Composition (WSC) provides a flexible framework for integrating independent web services to satisfy complex user requirements. WSC aims to choose the best web service from a set of candidates. The candidates have the same functionality and different non-functional criteria such as Quality of Service (QoS). In this work, the authors propose an ant-inspired algorithm for such problem. They named it Flying Ant Colony Optimization (FACO). Flying ants inject pheromone not only on the nodes on their paths but also on neighboring nodes increasing their chances of being explored in future iterations. The amount of pheromone deposited on these neighboring nodes is inversely proportional to the distance between them and the nodes on the path. The authors believe that by depositing pheromone on neighboring nodes, FACO may consider a more diverse population of solutions, which may avoid stagnation. The empirical experiments show that FACO outperform Ant Colony Optimization (ACO) for the WSC problem, in terms of the quality of solutions but it requires slightly more execution time.

scholarly journals Test Suite Minimization using Hybrid Algorithm for GA generated Test Cases

2013 ◽  
Vol 6 (1) ◽  
pp. 279-286 ◽  
Author(s):  
Mrs. P Maragathavalli ◽  
S. Kanmani
Keyword(s):  
Test Data ◽  
Execution Time ◽  
Hybrid Algorithm ◽  
Mutant Gene ◽  
Test Suite ◽  
Test Cases ◽  
Testing Time ◽  
Test Data Generation ◽  
Data Generation ◽  
Minimization Procedure

Software testing and retesting occurs continuously during the software development lifecycle to detect errors as early as possible. As the software evolves the size of test suites also grows. When the no of test cases generated are more, obviously size of the test suite will also be more.  So the testing time is to be minimized by reducing the execution time of the algorithm used for test data generation and also by introducing minimization procedure for test suite reduction. Due to limited resources and timing constraints for testing, test suite minimization techniques are needed to eliminate redundant test cases as possible. By considering multiple objectives rather than the coverage alone, the test cases are being generated which satisfies the testing requirements. Most of the existing techniques are code-based. In this article we present an approach by modifying an existing heuristic for test suite minimization.  Genetic algorithm has been used for random test data generation and the output of GA is given to the minimization procedure for reducing the total no of generated test cases, collectively named as Hybrid Algorithm (HA). The results are satisfactory and show significant improvements in reducing test suite size with minimum execution time. Experiments have been done for simple to medium complexity java programs taken from SIR and execution time is reduced to 5,685ms for a test set. The results are compared with existing method Mutant Gene Algorithm and size of test suite is minimized upto 13.6% using Hybrid Algorithm.

Sign in / Sign up

Export Citation Format

Share Document