Test Suite Optimization Using Chaotic Firefly Algorithm in Software Testing

2021 ◽  
pp. 722-739
Author(s):  
Abhishek Pandey ◽  
Soumya Banerjee
Keyword(s):  
Software Testing ◽  
Firefly Algorithm ◽  
Chaotic Behavior ◽  
Automatic Generation ◽  
Test Suite ◽  
Branch Coverage ◽  
Bee Colony ◽  
Novel Approach ◽  
Primary Focus ◽  
Test Suite Optimization

Software testing is time consuming and a costly activity. Effective generation of test cases is necessary in order to perform rigorous testing. There exist various techniques for effective test case generation. These techniques are based on various test adequacy criteria such as statement coverage, branch coverage etc. Automatic generation of test data has been the primary focus of software testing research in recent past. In this paper a novel approach based on chaotic behavior of firefly algorithm is proposed for test suite optimization. Test suite optimization problem is modeled in the framework of firefly algorithm. An Algorithm for test optimization based on firefly algorithm is also proposed. Experiments are performed on some benchmark Program and simulation results are compared for ABC algorithm, ACO algorithm, GA with Chaotic firefly algorithm. Major research findings are that chaotic firefly algorithm outperforms other bio inspired algorithm such as artificial bee colony, Ant colony optimization and Genetic Algorithm in terms of Branch coverage in software testing.

Test Suite Optimization Using Chaotic Firefly Algorithm in Software Testing

2017 ◽  
Vol 8 (4) ◽  
pp. 41-57 ◽  
Author(s):  
Abhishek Pandey ◽  
Soumya Banerjee
Keyword(s):  
Software Testing ◽  
Firefly Algorithm ◽  
Chaotic Behavior ◽  
Automatic Generation ◽  
Test Suite ◽  
Test Case ◽  
Branch Coverage ◽  
Bee Colony ◽  
Novel Approach ◽  
Test Suite Optimization

Software testing is time consuming and a costly activity. Effective generation of test cases is necessary in order to perform rigorous testing. There exist various techniques for effective test case generation. These techniques are based on various test adequacy criteria such as statement coverage, branch coverage etc. Automatic generation of test data has been the primary focus of software testing research in recent past. In this paper a novel approach based on chaotic behavior of firefly algorithm is proposed for test suite optimization. Test suite optimization problem is modeled in the framework of firefly algorithm. An Algorithm for test optimization based on firefly algorithm is also proposed. Experiments are performed on some benchmark Program and simulation results are compared for ABC algorithm, ACO algorithm, GA with Chaotic firefly algorithm. Major research findings are that chaotic firefly algorithm outperforms other bio inspired algorithm such as artificial bee colony, Ant colony optimization and Genetic Algorithm in terms of Branch coverage in software testing.

Test Suite Optimization Using Firefly and Genetic Algorithm

2022 ◽  
pp. 1635-1651
Author(s):  
Abhishek Pandey ◽  
Soumya Banerjee
Keyword(s):  
Genetic Algorithm ◽  
Software Engineering ◽  
Software Testing ◽  
Hybrid Approach ◽  
Empirical Evaluation ◽  
Data Generation ◽  
Testing Environment ◽  
Novel Approach ◽  
Test Suite Optimization

Software testing is essential for providing error-free software. It is a well-known fact that software testing is responsible for at least 50% of the total development cost. Therefore, it is necessary to automate and optimize the testing processes. Search-based software engineering is a discipline mainly focussed on automation and optimization of various software engineering processes including software testing. In this article, a novel approach of hybrid firefly and a genetic algorithm is applied for test data generation and selection in regression testing environment. A case study is used along with an empirical evaluation for the proposed approach. Results show that the hybrid approach performs well on various parameters that have been selected in the experiments.

scholarly journals Automated Generation of Independent Paths and Test Suite Optimization Using Artificial Bee Colony

2012 ◽  
Vol 30 ◽  
pp. 191-200 ◽  
Author(s):  
Soma Sekhara Babu Lam ◽  
M L Hari Prasad Raju ◽  
Uday Kiran M ◽  
Swaraj Ch ◽  
Praveen Ranjan Srivastav
Keyword(s):  
Artificial Bee Colony ◽  
Test Suite ◽  
Automated Generation ◽  
Bee Colony ◽  
Test Suite Optimization

scholarly journals Genetic Algorithm for Test Suite Optimization: An Experimental Investigation of Different Selection Methods

2021 ◽  
Vol 12 (3) ◽  
pp. 3778-3787
Author(s):  
Chetan J. Shingadiya Et.al
Keyword(s):  
Genetic Algorithm ◽  
Experimental Investigation ◽  
Software Testing ◽  
Test Suite ◽  
Test Case ◽  
Testing Time ◽  
Software Product ◽  
Tournament Selection ◽  
Test Suite Optimization ◽  
Test Suites

Software Testing is an important aspect of the real time software development process. Software testing always assures the quality of software product. As associated with software testing, there are few very important issues where there is a need to pay attention on it in the process of software development test. These issues are generation of effective test case and test suite as well as optimization of test case and suite while doing testing of software product. The important issue is that testing time of the test case and test suite. It is very much important that after development of software product effective testing should be performed. So to overcome these issues of optimization, we have proposed new approach for test suite optimization using genetic algorithm (GA). Genetic algorithm is evolutionary in nature so it is often used for optimization of problem by researcher. In this paper, our aim is to study various selections methods like tournament selection, rank selection and roulette wheel selection and then we apply this genetic algorithm (GA) on various programs which will generate optimized test suite with parameters like fitness value of test case, test suite and take minimum amount of time for execution after certain preset generation. In this paper our main objectives as per the experimental investigation, we show that tournament selection works very fine as compared to other methods with respect fitness selection of test case and test suites, testing time of test case and test suites as well as  number of requirements.

Test Suite Optimization Using Firefly and Genetic Algorithm

2019 ◽  
Vol 11 (1) ◽  
pp. 31-46 ◽  
Author(s):  
Abhishek Pandey ◽  
Soumya Banerjee
Keyword(s):  
Genetic Algorithm ◽  
Software Engineering ◽  
Software Testing ◽  
Hybrid Approach ◽  
Empirical Evaluation ◽  
Data Generation ◽  
Testing Environment ◽  
Novel Approach ◽  
Test Suite Optimization

Software testing is essential for providing error-free software. It is a well-known fact that software testing is responsible for at least 50% of the total development cost. Therefore, it is necessary to automate and optimize the testing processes. Search-based software engineering is a discipline mainly focussed on automation and optimization of various software engineering processes including software testing. In this article, a novel approach of hybrid firefly and a genetic algorithm is applied for test data generation and selection in regression testing environment. A case study is used along with an empirical evaluation for the proposed approach. Results show that the hybrid approach performs well on various parameters that have been selected in the experiments.

An Efficient Method to Generate Test Data for Software Structural Testing Using Artificial Bee Colony Optimization Algorithm

2017 ◽  
Vol 27 (06) ◽  
pp. 951-966 ◽  
Author(s):  
Zohreh Karimi Aghdam ◽  
Bahman Arasteh
Keyword(s):  
Software Testing ◽  
Test Data ◽  
Large Scale ◽  
Artificial Bee Colony ◽  
Fitness Function ◽  
Structural Testing ◽  
Branch Coverage ◽  
Bee Colony ◽  
Coverage Criterion

Software testing is a process for determining the quality of software system. Many small and medium-sized software projects can be manually tested. Nevertheless, due to the widespread extension of software in large-scale projects, testing them will be highly time consuming and costly. Hence, automated software testing (AST) is considered to be as a solution which can ease and simplify heavy and cumbersome tasks involved in software testing. For AST, certain data are needed through which the quality of systems can be evaluated. In this paper, an artificial bee colony (ABC) algorithm was used for solving the issue of test data generation and branch coverage criterion was used as a fitness function for optimizing the proposed solutions. For doing comparisons, seven well-known and traditional programs in the literature were used as benchmarks. The experimental results indicate that our method, on average, outperforms simulated annealing, genetic algorithm, particle swarm optimization and ant colony optimization based on the following four criteria: 99.99% average branch coverage, 99.94% success rate, 3.59 average convergence generation and 0.18[Formula: see text]ms average execution time.

scholarly journals Automatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm

2020 ◽  
Vol 30 (1) ◽  
pp. 59-72
Author(s):  
P Lakshminarayana ◽  
T V SureshKumar
Keyword(s):  
Software Development ◽  
Software Testing ◽  
Test Data ◽  
Cuckoo Search ◽  
Optimization Method ◽  
Automatic Generation ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Bee Colony

AbstractSoftware testing is a very important technique to design the faultless software and takes approximately 60% of resources for the software development. It is the process of executing a program or application to detect the software bugs. In software development life cycle, the testing phase takes around 60% of cost and time. Test case generation is a method to identify the test data and satisfy the software testing criteria. Test case generation is a vital concept used in software testing, that can be derived from the user requirements specification. An automatic test case technique determines automatically where the test cases or test data generates utilizing search based optimization method. In this paper, Cuckoo Search and Bee Colony Algorithm (CSBCA) method is used for optimization of test cases and generation of path convergence within minimal execution time. The performance of the proposed CSBCA was compared with the performance of existing methods such as Particle Swarm Optimization (PSO), Cuckoo Search (CS), Bee Colony Algorithm (BCA), and Firefly Algorithm (FA).

A Novel Approach to Component-Based Software Testing

2014 ◽  
Vol 39 (6) ◽  
pp. 1-4 ◽  
Author(s):  
Lata Nautiyal ◽  
Neena Gupta ◽  
Sushil Chandra Dimri
Keyword(s):  
Software Testing ◽  
Novel Approach

scholarly journals Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1779
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang ◽  
Chi-En Tsai
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Random Search ◽  
Automatic Generation ◽  
Superior Performance ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Network Systems ◽  
Routing Problem

Test case generation is an important process in software testing. However, manual generation of test cases is a time-consuming process. Automation can considerably reduce the time required to create adequate test cases for software testing. Genetic algorithms (GAs) are considered to be effective in this regard. The multiple-searching genetic algorithm (MSGA) uses a modified version of the GA to solve the multicast routing problem in network systems. MSGA can be improved to make it suitable for generating test cases. In this paper, a new algorithm called the enhanced multiple-searching genetic algorithm (EMSGA), which involves a few additional processes for selecting the best chromosomes in the GA process, is proposed. The performance of EMSGA was evaluated through comparison with seven different search-based techniques, including random search. All algorithms were implemented in EvoSuite, which is a tool for automatic generation of test cases. The experimental results showed that EMSGA increased the efficiency of testing when compared with conventional algorithms and could detect more faults. Because of its superior performance compared with that of existing algorithms, EMSGA can enable seamless automation of software testing, thereby facilitating the development of different software packages.

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