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.

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.

TEST DATA GENERATION FOR SOFTWARE TESTING BASED ON QUANTUM-INSPIRED GENETIC ALGORITHM

2013 ◽  
Vol 12 (01) ◽  
pp. 1350004 ◽  
Author(s):  
CHENGYING MAO ◽  
XINXIN YU
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Test Data ◽  
Optimal Solution ◽  
Search Space ◽  
Structural Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
Quantum Bit

The quality of test data has an important impact on the effect of software testing, so test data generation has always been a key task for finding the potential faults in program code. In structural testing, the primary goal is to cover some kinds of structure elements with some specific inputs. Search-based test data generation provides a rational way to handle this difficult problem. In the past, some well-known meta-heuristic search algorithms have been successfully utilized to solve this issue. In this paper, we introduce a variant of genetic algorithm (GA), called quantum-inspired genetic algorithm (QIGA), to generate the test data with stronger coverage ability. In this new algorithm, the traditional binary bit is replaced by a quantum bit (Q-bit) to enlarge the search space so as to avoid falling into local optimal solution. On the other hand, some other strategies such as quantum rotation gate and catastrophe operation are also used to improve algorithm efficiency and quality of test data. In addition, experimental analysis on eight real-world programs is performed to validate the effectiveness of our method. The results show that QIGA-based method can generate test data with higher coverage in much smaller convergence generations than GA-based method. More importantly, our proposed method is more robust for algorithm parameter change.

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.

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 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.

scholarly journals A Hybrid Approach Combining Fuzzy c-Means-Based Genetic Algorithm and Machine Learning for Predicting Job Cycle Times for Semiconductor Manufacturing

2021 ◽  
Vol 11 (16) ◽  
pp. 7428
Author(s):  
Gyu M. Lee ◽  
Xuehong Gao
Keyword(s):  
Genetic Algorithm ◽  
Cycle Time ◽  
Hybrid Approach ◽  
Cycle Times ◽  
Fuzzy C Means ◽  
Backpropagation Network ◽  
Semiconductor Fabrication ◽  
Time Required ◽  
And Training

Job cycle time is the cycle time of a job or the time required to complete a job. Prediction of job cycle time is a critical task for a semiconductor fabrication factory. A predictive model must forecast job cycle time to pursue sustainable development, meet customer requirements, and promote downstream operations. To effectively predict job cycle time in semiconductor fabrication factories, we propose an effective hybrid approach combining the fuzzy c-means (FCM)-based genetic algorithm (GA) and a backpropagation network (BPN) to predict job cycle time. All job records are divided into two datasets: the first dataset is for clustering and training, and the other is for testing. An FCM-based GA classification method is developed to pre-classify the first dataset of job records into several clusters. The classification results are then fed into a BPN predictor. The BPN predictor can predict the cycle time and compare it with the second dataset. Finally, we present a case study using the actual dataset obtained from a semiconductor fabrication factory to demonstrate the effectiveness and efficiency of the proposed approach.

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