Automatic Data Flow Test Paths Generation using the Genetical Swarm Optimization Technique

Girgis, Moheb R.; Ghiduk, Ahmed S.; Abd-elkawy, Eman H.

doi:10.5120/20469-2324

Cited by 12 publications

(5 citation statements)

References 29 publications

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“…Algorithm 2. Pseudocode of APGWO [39] Initialize The solution of the APGWO-wrapper is a binary array having a dimension of 1×n, where n is the total number of features. Selected features will take a value of 1, and 0 otherwise.…”

Section: ) Adaptive Particle -Grey Wolf Optimization (Apgwo)mentioning

confidence: 99%

A Novel Wrapper–Based Feature Selection for Early Diabetes Prediction Enhanced With a Metaheuristic

Pham

et al. 2021

IEEE Access

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Diabetes leads to health problems for hundreds of millions of people globally every year. Available medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at finding patterns or features undetectable by current practice. In this work, we proposed a machine learning model to predict the early onset of diabetes patients. It is a novel wrapper-based feature selection utilizing Grey Wolf Optimization (GWO) and an Adaptive Particle Swam Optimization (APSO) to optimize the Multilayer Perceptron (MLP) to reduce the number of required input attributes. Moreover, we also compared the results achieved using this method and several conventional machine learning algorithms approaches such as Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Naïve Bayesian Classifier (NBC), Random Forest Classifier (RFC), Logistic Regression (LR). Computational results of our proposed method show not only that much fewer features are needed, but also higher prediction accuracy can be achieved (96% for GWO -MLP and 97% for APGWO -MLP). This work has the potential to be applicable to clinical practice and become a supporting tool for doctors/physicians.

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Section: ) Adaptive Particle -Grey Wolf Optimization (Apgwo)mentioning

confidence: 99%

A Novel Wrapper–Based Feature Selection for Early Diabetes Prediction Enhanced With a Metaheuristic

Pham

et al. 2021

IEEE Access

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“…Chawla et al [20] proposed a hybrid PSO and GA algorithm for automatic generation of test suites with branch coverage as the test adequacy criterion. The experiments are performed with ten Java container classes.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, overall performance of the proposed hybrid (adaptive PSO and DE) algorithm is compared with DE, PSO, GA and random search with respect to the measures collected. Tables 7 -10, as given below, summarize the results of applying the various testing approaches to the set of chosen subject programs for (10,15,20,25). Range of the input integer variables is taken to be 0-100; range is different for variables of Program# 3, 4, and 7 as per the requirement of each program.…”

Section: Overall Comparisonmentioning

confidence: 99%

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A Hybrid Particle Swarm Optimization and Differential Evolution based Test Data Generation Algorithm for Data-Flow Coverage using Neighbourhood Search Strategy

Varshney

Mehrotra

2018

IJCAI

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Meta-heuristic search techniques, mainly Genetic Algorithm (GA), have been widely applied for automated test data generation according to a structural test adequacy criterion. However, it remains a challenging task for more robust adequacy criterion such as data-flow coverage of a program. Now, focus is on the use of other highly-adaptive meta-heuristic search techniques such as Particle Swarm Optimization (PSO) and Differential Evolution (DE). In this paper, a hybrid (adaptive PSO and DE) algorithm is proposed to generate test data for data-flow dependencies of a program with a neighbourhood search strategy to improve the search capability of the hybrid algorithm. The fitness function is based on the concepts of dominance relations and branch distance. The measures considered are mean number of generations and mean percentage coverage. The performance of the hybrid algorithm is compared with that of DE, PSO, GA, and random search. Over several experiments on a set of benchmark programs, it is shown that the hybrid algorithm performed significantly better than DE, PSO, GA and random search in data-flow test data generation with respect to the measures collected. Povzetek: Razvit je nov algoritem kot kombinacija hibridnega roja delcev in diferenčne evolucije z uporabo sosednje iskalne strategije.

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“…Ding et al [ 17] divided the population of GA into several swarms which were impacted by particle swarm optimisation (PSO) to increase the diversity of the population. Similarly, Girgis et al [18] composited GA and PSO to solve ATCG-PC. Srivastava et al [ 19] proposed a meta-heuristic algorithm of GA and ant colony optimisation (ACO) to increase the discovering rate of the uncovered paths.…”

Section: Introductionmentioning

confidence: 99%

Automated test data generation based on particle swarm optimisation with convergence speed controller

Liu

Huang

et al. 2017

CAAI Transactions on Intelligence Technology

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Automated test data generation for path coverage (ATDG-PC) plays an important role in software testing. In this study, ATDG-PC is applied to the case of cloud computing such as Hadoop programmes which are more difficult to search for high-rate path coverage than the normal programmes. The search scale of ATDG-PC is usually enormous, while the relationship between the variables and the paths is unknown. First, a rapid metaheuristic algorithm particle swarm optimisation (PSO) was chosen to solve the problem of large-scale search. Second, the strategy of convergence speed controller was used to improve the performance of PSO by mining heuristic information from the found paths. The controller adjusts the convergence speed balance periodically by two conditions and rules. The first strategy slows the convergence speed when the algorithm is premature convergence and is trapped in a local optimum. The second strategy accelerates the convergence speed if the algorithm does not converge after many iterations. The effectiveness of the proposed algorithm is evaluated by classic Hadoop programmes of cloud computing. The experimental results indicate that the proposed algorithm can reduce a great number of test cases for path coverage, compared with other metaheuristic algorithms for automated test data generation.

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Automatic Data Flow Test Paths Generation using the Genetical Swarm Optimization Technique

Cited by 12 publications

References 29 publications

A Novel Wrapper–Based Feature Selection for Early Diabetes Prediction Enhanced With a Metaheuristic

A Novel Wrapper–Based Feature Selection for Early Diabetes Prediction Enhanced With a Metaheuristic

A Hybrid Particle Swarm Optimization and Differential Evolution based Test Data Generation Algorithm for Data-Flow Coverage using Neighbourhood Search Strategy

Automated test data generation based on particle swarm optimisation with convergence speed controller

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