scholarly journals A Test Cases Generation Method for Industrial Control Protocol Test

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wenli Shang ◽  
Guanyu Zhang ◽  
Tianyu Wang ◽  
Rui Zhang
Keyword(s):  
Genetic Algorithm ◽  
Use Cases ◽  
Test Cases ◽  
Industrial Control ◽  
Important Indicator ◽  
Fitness Functions ◽  
Modbus Protocol ◽  
Dispersion Degree ◽  
Important Research Topic ◽  
Population Dispersion

The coverage of test cases is an important indicator for the security and robustness test of industrial control protocols. It is an important research topic to complete the test with less use cases. Taking Modbus protocol as an example, a calculation method of case similarity and population dispersion based on weight division is proposed in this paper. The method can describe the similarity of use cases and the dispersion degree of individuals in the population more accurately. Genetic algorithm is used to generate and optimize test cases, and individual similarity and population dispersion are used as fitness functions of genetic algorithm. Experimental results show that the proposed method can increase the population dispersion by 3.45% compared with the conventional methods and effectively improve the coverage of test cases.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

Generating test cases and procedures from use cases in dynamic software product lines

2017 ◽  
Author(s):  
Italo L. Araújo ◽  
Ismayle S. Santos ◽  
João B. Ferreira Filho ◽  
Rossana M. C. Andrade ◽  
Pedro Santos Neto
Keyword(s):  
Software Product Lines ◽  
Use Cases ◽  
Test Cases ◽  
Product Lines ◽  
Software Product ◽  
Dynamic Software

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.

scholarly journals Performance of the Modified Dolphin Monitoring Operator for Weight Optimization of Skeletal Structures

2018 ◽  
Author(s):  
Ali Kaveh ◽  
S.R. Hoseini Vaez ◽  
Pedram Hosseini
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Structural Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Harmony Search ◽  
Average Weight ◽  
Weight Optimization ◽  
Swarm Optimization ◽  
Population Dispersion

In this study, the Modified Dolphin Monitoring (MDM) operator is used to enhance the performance of some metaheuristic algorithms. The MDM is a recently presented operator that controls the population dispersion in each iteration. Algorithms are selected from some well-established algorithms. Here, this operator is applied on Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Vibrating Particles System (VPS), Enhanced Vibrating Particles System (EVPS), Colliding Bodied Optimization (CBO) and Harmony Search (HS) and the performance of these algorithms are evaluated with and without this operator on three well-known structural optimization problems. The results show the performance of this operator on these algorithms for the best, the worst, average and average weight of the first quarter of answers.

scholarly journals A Prioritization Approach for Regression Test Cases Based on a Revised Genetic Algorithm

2021 ◽  
Vol 50 (3) ◽  
pp. 443-457
Author(s):  
Thamer Alrawashdeh ◽  
Fuad ElQirem ◽  
Ahmad Althunibat ◽  
Roba Alsoub
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Regression Testing ◽  
Test Cases ◽  
Average Percentage ◽  
Lower Priority ◽  
Regression Test ◽  
Predefined Criterion ◽  
Good Coverage ◽  
Selection Of

The regression testing is a software-based testing approach executed to verify that changes made to the softwaredo not affect the existing functionality of the product. On account of the constraints of time and cost, it isimpractical to re-execute all the test cases for software whenever a change occurs. In order to overcome sucha problem in the selection of regression test cases, a prioritization technique should be employed. On the basisof some predefined criterion, the prioritization techniques create an execution schedule for the test cases, sothe higher priority test cases can be performed earlier than the lower priority test cases in order to improvethe efficiency of the software testing. Many prioritization criteria for regression test cases have been proposedin software testing literature; however, most of such techniques are code-based. Keeping in view this fact, thisresearch work has proposed a prioritization approach for regression test cases generated from software specificationswhich are based on the criterion of the Average Percentage Transition Coverage (APTC) by using arevised genetic algorithm. This criterion evaluates the rate of transitions coverage by incorporating knowledgeabout the significance of transitions between activates in the form of weights. APTC has been used as a fitnessevaluation function in a genetic algorithm to measure the effectiveness of a test cases sequence. Moreover, inorder to improve the coverage percentage, the proposed approach has revised the genetic algorithm by solvingthe problem of the optimal local solution. The experimental results show that the proposed approach demonstratesa good coverage performance with less execution time as compared to the standard genetic algorithmand some other prioritization techniques.

Proposal of Iterative Genetic Algorithm for Test Suite Generation

2021 ◽  
Vol 12 (1) ◽  
pp. 111-130
Author(s):  
Ankita Bansal ◽  
Abha Jain ◽  
Abhijeet Anand ◽  
Swatantra Annk
Keyword(s):  
Genetic Algorithm ◽  
Execution Time ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Computation Complexity ◽  
Test Suite Generation ◽  
Adaptive Nature ◽  
Efficiency And Reliability

Huge and reputed software industries are expected to deliver quality products. However, industry suffers from a loss of approximately $500 billion due to shoddy software quality. The quality of the product in terms of its accuracy, efficiency, and reliability can be revamped through testing by focusing attention on testing the product through effective test case generation and prioritization. The authors have proposed a test-case generation technique based on iterative listener genetic algorithm that generates test cases automatically. The proposed technique uses its adaptive nature and solves the issues like redundant test cases, inefficient test coverage percentage, high execution time, and increased computation complexity by maintaining the diversity of the population which will decrease the redundancy in test cases. The performance of the technique is compared with four existing test-case generation algorithms in terms of computational complexity, execution time, coverage, and it is observed that the proposed technique outperformed.

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