scholarly journals Coevolution of Second-order-mutant

2018 ◽  
Vol 8 (5) ◽  
pp. 3238
Author(s):  
Mohamad Syafri Tuloli ◽  
Benhard Sitohang ◽  
Bayu Hendradjaya
Keyword(s):  
Genetic Algorithm ◽  
Optimization Method ◽  
Mutation Testing ◽  
Second Order ◽  
Real Case ◽  
Test Case ◽  
Test Cases ◽  
Single Population ◽  
Testing Problem

<span>One of the obstacles that hinder the usage of mutation testing is its impracticality, two main contributors of this are a large number of mutants and a large number of test cases involves in the process. Researcher usually tries to address this problem by optimizing the mutants and the test case separately. In this research, we try to tackle both of optimizing mutant and optimizing test-case simultaneously using a coevolution optimization method. The coevolution optimization method is chosen for the mutation testing problem because the method works by optimizing multiple collections (population) of a solution. This research found that coevolution is better suited for multi-problem optimization than other single population methods (i.e. Genetic Algorithm), we also propose new indicator to determine the optimal coevolution cycle. The experiment is done to the artificial case, laboratory, and also a real case.</span>

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.

A Analysis and Research on the Methods of Object Oriented Software Testing

2013 ◽  
Vol 709 ◽  
pp. 616-619
Author(s):  
Jing Chen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Test Data ◽  
Object Oriented ◽  
Automatic Generation ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Path Coverage ◽  
Parameter Values

This paper proposes a genetic algorithm-based method to generate test cases. This method provides information for test case generation using state machine diagrams. Its feature is realizing automation through fewer generated test cases. In terms of automatic generation of test data based on path coverage, the goal is to build a function that can excellently assess the generated test data and guide the genetic algorithms to find the targeting parameter values.

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 K-Step Crossover Method based on Genetic Algorithm for Test Suite Prioritization in Regression Testing

2021 ◽  
Vol 27 (2) ◽  
pp. 170-189
Author(s):  
P. K. Gupta
Keyword(s):  
Genetic Algorithm ◽  
Regression Testing ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Average Percentage ◽  
Code Coverage ◽  
Test Case Selection ◽  
Case Selection

Software is an integration of numerous programming modules&nbsp; (e.g., functions, procedures, legacy system, reusable components, etc.) tested and combined to build the entire module. However, some undesired faults may occur due to a change in modules while performing validation and verification. Retesting of entire software is a costly affair in terms of money and time. Therefore, to avoid retesting&nbsp;of entire&nbsp;software, regression testing is performed. In regression testing, an earlier created test suite is used to retest the software system&#39;s modified module. Regression Testing works in three manners; minimizing test cases, selecting test cases, and prioritizing test cases. In this paper, a two-phase algorithm has been proposed that considers test case selection and test case prioritization technique for performing regression testing on several modules ranging from a smaller line of codes to huge line codes of procedural language. A textual based differencing algorithm has been implemented for test case selection. Program statements modified between two modules are used for textual differencing and utilized to identify test cases that affect modified program statements. In the next step, test case prioritization is implemented by applying the Genetic Algorithm for code/condition coverage. Genetic operators: Crossover and Mutation have been applied over the initial population (i.e. test cases), taking code/condition coverage as fitness criterion to provide a prioritized test suite. Prioritization algorithm can be applied over both original and reduced test suite depending upon the test suite&#39;s size or the need for accuracy. In the obtained results, the efficiency of the prioritization algorithms has been analyzed by the Average Percentage of Code Coverage (APCC) and Average Percentage of Code Coverage with cost (APCCc). A comparison of the proposed approach is also done with the previously proposed methods and it is observed that APCC &amp; APCCc values achieve higher percentage values faster in the case of the prioritized test suite in contrast to the non-prioritized test suite.

Cloud Computing Environment Based on Web Log Mining Algorithm Implementation of Test

2013 ◽  
Vol 760-762 ◽  
pp. 1293-1297
Author(s):  
Bin Wang ◽  
Yong Cheng Jiang ◽  
Jing Li
Keyword(s):  
Genetic Algorithm ◽  
Software Test ◽  
Test Case ◽  
Test Cases ◽  
Test Environment ◽  
Cloud Computing Environment ◽  
Material Resources ◽  
Complex Process ◽  
Important Means ◽  
Algorithm Implementation

Software test is the important means that guarantee software quality and reliability, and in this respect,it plays the role that other method cannot replace. However software test is a complex process , it needs to consume huge manpower,material resources and time,which takes the 40%~50% of entire software development cost approximately . Paper presents the inherent in software test case designing based on genetic algorithm is using genetic algorithm to solve a set of optimization test cases, and the framework includes three parts which are test environment construction, genetic algorithm and the environment for test .

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 Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases

2020 ◽  
Vol 10 (20) ◽  
pp. 7264
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Execution Time ◽  
Multicast Routing ◽  
Test Case ◽  
Test Cases ◽  
Mutation Operator ◽  
Optimal Solutions ◽  
Crossover Operator ◽  
Network Systems

Software testing using traditional genetic algorithms (GAs) minimizes the required number of test cases and reduces the execution time. Currently, GAs are adapted to enhance performance when finding optimal solutions. The multiple-searching genetic algorithm (MSGA) has improved upon current GAs and is used to find the optimal multicast routing in network systems. This paper presents an analysis of the optimization of test case generations using the MSGA by defining suitable values of MSGA parameters, including population size, crossover operator, and mutation operator. Moreover, in this study, we compare the performance of the MSGA with a traditional GA and hybrid GA (HGA). The experimental results demonstrate that MSGA reaches the maximum executed branch statements in the lowest execution time and the smallest number of test cases compared to the GA and HGA.

Generating Test Case for Object-Oriented Software Using Genetic Algorithm and Mutation Testing Method

2012 ◽  
Vol 3 (1) ◽  
pp. 15-23 ◽  
Author(s):  
Yamina Mohamed Ben Ali ◽  
Fatma Benmaiza
Keyword(s):  
Genetic Algorithm ◽  
Score Function ◽  
Mutation Testing ◽  
Software Test ◽  
Test Case ◽  
Optimal Test ◽  
Genetic Population ◽  
Input Program ◽  
Oriented Object ◽  
First Time

This paper presents an automatic creation of software test cases based on the use of a genetic algorithm and a mutation testing technique. The aim of this work is then the optimization of a score function in order to give the best set of optimal test case needed for testing an oriented-object program. Therefore, the proposed search-based approach generates in a first time a set of mutants according to an input program for testing the output of methods belonging in the tested class. On the second time, the output of the mutants and the input program are compared to evaluate the performance of all chromosomes in the genetic population. Finally, at the end of the chromosomes evolution the best test case in retrieved as the optimal one.

A Method of Testing Generation Based on Prioritized Pair

2012 ◽  
Vol 241-244 ◽  
pp. 2696-2700
Author(s):  
Yu Wang ◽  
Hao Wu ◽  
Zhen Yu Sheng
Keyword(s):  
Genetic Algorithm ◽  
Detection Rate ◽  
Test Methods ◽  
Limited Resources ◽  
Test Case ◽  
Test Cases ◽  
Test Coverage ◽  
Combinatorial Testing ◽  
Critical Test ◽  
Generate Pair

Combinatorial testing has lots of test cases, but software testers hope to get the best test coverage with the smallest test case suite. For the scale of produced test cases is so large that researchers have considered the implementation of the critical test cases. This article researches the classic combinatorial test methods and proposes methods to generate pair-wise testing cases with a priority. Firstly, we design formulas to compute the weights of priorities. Secondly, we adopt a greed algorithm to solve the combinatorial testing problems. Furthermore, we integrate the greed strategy into a genetic algorithm to improve the efficiency. It improves the testing efficiency while securing the detection rate of defects under limited resources.

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