scholarly journals Automatic Test Cases Generation using Multistage-Based Genetic Algorithm for Object Oriented Testing

2019 ◽  
Vol 8 (2S10) ◽  
pp. 561-573
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Research Work ◽  
Test Case ◽  
Test Cases ◽  
Automatic Test ◽  
Accuracy Rate ◽  
Multi Stage ◽  
Time Period

Software testing is a major phase that takes place under the construction of software designing. Basically, testing is a process that assists in the determination of work that it reached to the desired output or not. It generally depends on the validation and verification procedure, whereas in simple terms a software testing process is to discover the bugs, errors, faults of the developed software and manage it. It is also considered as the risk based activity. The testing criterion is different at each level and it is completed in various steps. The life cycle of software testing is composed of various steps as the feasibility study, data gathering and specification, design or framework, unit testing, integration and system testing. At last the maintenance is occurring to finalize the software application. In software engineering several kinds of testing strategies are utilized as black box, white box, regression testing, static, dynamic and so on. There are enormous advantages of software testing. The common advantages are to investigate software quality, access the huge pool for verification, deducted the construction cost, improve the reusability, aimed at the basic competencies, increase the demand of the product, balance the time period for the development of software and boost the competitiveness. But there are also certain vulnerabilities related to the large investments, software tools, training, need of more manpower, most time consuming of test preparations, need of more testing space, hidden errors impact on the entire code and cost. In the proposed work, the performance is reliant on the better way. Test case generation is a procedure to generate software corresponding various test case generations and validate various test cases. So that research work identifies the quality of software. This process also declined the maintenance cost (MC) of a software system. In the proposed architecture design, Multi-stage Genetic algorithm has various benefits as it is highly effective in higher dimensional spaces, more memory efficient and versatile. Basically, Multi-stage GA is applied in several real-time applications as in the text categorization, classification of test cases and regression related issues. In the research work, mutants compare various existing techniques and performance parameters are like as mutants, accuracy rate, time consumption and number of events. The planned approach is best in terms to enhance the accuracy rate and achieved it in a reduced time period. Several techniques are used to compare the number of events fire. So that, the architecture accuracy rate has achieved this based on the number of events. The multistage GA test case is an intelligent approach and supportive to various languages like .Net, Java, C++ and Project Management used in an automatic test case. It helps to improve the quality of software

Efficient Framework for Fully Automatic Test Case Generation and Prioritization Using Genetic Algorithm in Software Testing

2020 ◽  
Vol 17 (11) ◽  
pp. 5198-5204
Author(s):  
Seema Rani ◽  
Amandeep Kaur
Keyword(s):  
Genetic Algorithm ◽  
Software Development ◽  
Software Testing ◽  
Development Process ◽  
Regression Testing ◽  
Process Time ◽  
Test Case ◽  
Test Cases ◽  
Software Development Process ◽  
Automatic Test

Automation in software testing is significantly growing in recent situation. Most part of the system is automated with help of the software. Today every modern software developers are trying to automate the software development process as much as possible. Therefore to develop any software more skills and expertise are needed. For the software development process, testing of software is the exceedingly significant and considerable phase. Automatic test data generation had an essential function in specific area regarding software testing. Test case creation is technique of gathering the data which completes the testing standards, all criteria’s and conditions. During testing process, the software goes through frequent modifications. As a result, due to all of these modifications and repetitive retesting the cost of testing process increases. This is called regression testing. Regression testing requires more expertise, more effort, more time and more cost. Here to reduce the time and expenditure, many type of techniques are proposed. The changes in one test case will affect all the others test cases. To triumph over this problem, when the changes occurred in the software every the test case have to be retested repeatedly. And this problem leads to make the testing process time consuming with unnecessary increased cost. Here In this research paper, the work’s focal point on automatic test cases generation and prioritization with improved evolutionary genetic algorithm.

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.

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.

Research on Automatic Test Case Generation Framework for Java

2014 ◽  
Vol 568-570 ◽  
pp. 1488-1496
Author(s):  
Ming Gang Xu ◽  
Yong Min Mu ◽  
Zhi Hua Zhang ◽  
Ang Liu
Keyword(s):  
Software Testing ◽  
Automatic Generation ◽  
Difficult Problem ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Labor Costs ◽  
Automatic Test ◽  
Automatic Test Case Generation ◽  
Java Program

Automatic test case generation has been a hotspot and a difficult problem in the software testing, Accurately and efficiently generate test cases can improve the efficiency of software testing. Java programs have many characteristics such as encapsulation, inheritance, polymorphism and so on, it is convenient for software design and development, but to bring automated testing some difficulties. This article on the Java program of automatic test case generation method is studied and presents a framework for automatic generation of test cases. With this framework, test case suite will be generated quickly and accurately. Experimental results show that automatic Java test case generation framework can quickly and accurately generate test cases , reduce labor costs and improve efficiency.

scholarly journals TEST CASE PRIORTIZATION USING GENETIC ALGORITHM

2014 ◽  
pp. 151-154
Author(s):  
RUCHIKA MALHOTRA ◽  
ABHISHEK BHARADWAJ
Keyword(s):  
Genetic Algorithm ◽  
Software Development ◽  
Software Testing ◽  
Major Part ◽  
Test Case ◽  
Test Cases ◽  
Development Lifecycle ◽  
Software Development Lifecycle ◽  
The One

Software is built by human so it cannot be perfect. So in order to make sure that developed software does not do any unintended thing we have to test every software before launching it in the operational world. Software testing is the major part of software development lifecycle. Testing involves identifying the test cases which can find the errors in the program. Exhaustive testing is not a good idea to follow. It is very difficult and time consuming to perform. In this paper a technique has been proposed to do prioritize test cases according to their capability of finding errors. One which is more likely to find the errors has been assigned a higher priority and the one which is less likely to find the errors in the program has been assigned low priority. It is recommended to execute the test cases according their priority to find the errors.

scholarly journals Test Case Quality Factors: Content Analysis of Software Testing Websites

Webology ◽  
2021 ◽  
Vol 18 (SI01) ◽  
pp. 75-87
Author(s):  
Samera Obaid Barraood ◽  
Haslina Mohd ◽  
Fauziah Baharom
Keyword(s):  
Content Analysis ◽  
Software Testing ◽  
Point Of View ◽  
Test Case ◽  
Test Cases ◽  
Quality Factors ◽  
Quality Test ◽  
Software Products ◽  
The Moment

Software testing is anessentialprocess for ensuring thequality and reliability of software products. The efficiency of testing activities depends largely on the test case quality, which is considered as one of the major concerns of software testing. Unfortunately, at the moment there is no clear guideline that can be referred by software testers in producing good quality test cases. Hence, producing guideline is certainly required. To construct a pragmatic guideline, it is crucial to identify the factors that lead todesigninggood quality test cases. The existing test case quality factors are not comprehensive and need further investigation and improvement. Therefore,a content analysis was conducted to identify the test case qualityfactors from software testing experts point of view available in the software testing websites. The software testing websites provide explicit information about the quality of test cases in order to avoid the poor design of test cases. Thus, this study presents the outcomes of content analysis from 22 software testing websites which comprise of static content websites and blogs.Consequently, eight (8)factors and their corresponding 30 sub-factors were identified. Among the factors are documentation, manageability, maintainability, reusability, requirement quality, efficiency, tester knowledge, and effectiveness of test cases. These factors are useful to be referred by the practitioners in assuring the quality of the design test cases which implicitly can ensure the quality of the software products.

An Approach to Generate Test Cases by Multi-Path Based on Genetic Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3976-3979
Author(s):  
Yu Liu ◽  
Feng Qin Wang ◽  
Xiu Li Zhao
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Software Testing ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Test Environment ◽  
Quality And Reliability ◽  
Classification Procedures

Software testing is important to ensure the quality and reliability of the software.The improvement on the automation of test case generation is the entire key to improve the automation of the testing process.It helps a lot in the generation of test cases to construct multi-path model.It is based on genetic algorithm with three parts which are the test environment construction, the genetic algorithms and the operating environment.It’s feasibility and efficiency is verified by triangle classification procedures.

scholarly journals Prioritize the Sequence of Test Cases and Increase the DRE Rate by Implementing Orthogonal Array Testing Strategy

2021 ◽  
Author(s):  
Sangeetha M. ◽  
Malathi S
Keyword(s):  
Life Cycle ◽  
Software Testing ◽  
Error Detection ◽  
Orthogonal Array ◽  
Test Case ◽  
Test Cases ◽  
Test Plan ◽  
Testing Strategy ◽  
Life Cycle Process

Abstract Software testing is an emerging technology which is use to increase the rate of error detection as early as possible in the software testing life cycle process. Test case prioritization technique plays a crucial role in organizing the test cases in sequencing order both ascending and descending such that test cases having high priority or high severity are planned to get executed doing a proper risk-based analysis. This prioritization technique effectively addresses two important organization constraints namely “Time” and “Budget”, also improve the quality of service. The proposed work is all about how effectively we can sequence the application modules for testing during Test plan phase using fuzzy logic and how to write optimized test cases efficiently design during Orthogonal Array Test Strategy (OATS) in Test design phase of testing life Cycle.

scholarly journals On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1145 ◽  
Author(s):  
Shweta Rani ◽  
Bharti Suri ◽  
Rinkaj Goyal
Keyword(s):  
Genetic Algorithm ◽  
Test Data ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Data Generation ◽  
Data Generation ◽  
Test Execution ◽  
Mutation Strategy

Manual test case generation is an exhaustive and time-consuming process. However, automated test data generation may reduce the efforts and assist in creating an adequate test suite embracing predefined goals. The quality of a test suite depends on its fault-finding behavior. Mutants have been widely accepted for simulating the artificial faults that behave similarly to realistic ones for test data generation. In prior studies, the use of search-based techniques has been extensively reported to enhance the quality of test suites. Symmetry, however, can have a detrimental impact on the dynamics of a search-based algorithm, whose performance strongly depends on breaking the “symmetry” of search space by the evolving population. This study implements an elitist Genetic Algorithm (GA) with an improved fitness function to expose maximum faults while also minimizing the cost of testing by generating less complex and asymmetric test cases. It uses the selective mutation strategy to create low-cost artificial faults that result in a lesser number of redundant and equivalent mutants. For evolution, reproduction operator selection is repeatedly guided by the traces of test execution and mutant detection that decides whether to diversify or intensify the previous population of test cases. An iterative elimination of redundant test cases further minimizes the size of the test suite. This study uses 14 Java programs of significant sizes to validate the efficacy of the proposed approach in comparison to Initial Random tests and a widely used evolutionary framework in academia, namely Evosuite. Empirically, our approach is found to be more stable with significant improvement in the test case efficiency of the optimized test suite.

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