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 .

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.

Generation Technology of Test Cases of Object-Oriented Software

2013 ◽  
Vol 756-759 ◽  
pp. 2433-2437
Author(s):  
Jing Chen
Keyword(s):  
Genetic Algorithm ◽  
Search Algorithm ◽  
Object Oriented ◽  
Software Test ◽  
Test Case ◽  
Test Cases ◽  
Transition Diagram ◽  
Class Testing ◽  
Generating Method ◽  
Data Flow Testing

This paper analyzed the basic object-oriented concepts from the perspective of testing. The effects of the characteristics of object-oriented software on the software testing were discussed. The class testing method of object-oriented software was put forward. This method includes tests based on the state transition diagram and data flow testing on class. A integration testing of object-oriented software was put forward based on the event-driven characteristics of object-oriented software, and a data-generating method of software test based on genetic algorithm was provided. The test case generating technology of object-oriented software was discussed, which utilized an intercalation method of branch function and regarded the genetic algorithm as the core search 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.

Ship Pipe Routing Method Based on Genetic Algorithm and Cooperative Coevolution

2017 ◽  
Vol 33 (02) ◽  
pp. 122-134
Author(s):  
Zongran Dong ◽  
Yan Lin
Keyword(s):  
Genetic Algorithm ◽  
Cell Decomposition ◽  
Test Case ◽  
Combinatorial Explosion ◽  
Pipe Routing ◽  
Routing Method ◽  
Single Pipe ◽  
Algorithm Implementation ◽  
Crossover And Mutation ◽  
Practical Constraints

Pipe routing is one of the most time-consuming and complicated jobs in shipbuilding design. This article presents the automatic ship pipe routing method. To improve the efficiency of single pipe routing, the fixed-length encoding genetic algorithm (GA) is first used by connecting adjacent intermediate points with generated pipe segments according to the specific routing patterns. The crossover and mutation operations are designed on the basis of this encoding as well. In case of the routing for multi pipes or pipe with branches, cooperative coevolutionary GA is adopted to route pipes harmoniously and to reduce the risk of combinatorial explosion caused by the number of pipes. During algorithm implementation and the building of cell decomposition model, the practical constraints in ship piping have been taken into account. In the end, the efficiency and feasibility of the proposed approach are illustrated by solving problems in designed test case and real ship applications.

A Web Automation Testing Framework over Cloud

2014 ◽  
Vol 556-562 ◽  
pp. 6149-6153
Author(s):  
Min Gang Chen ◽  
Wen Bin Zhong ◽  
Wen Jie Chen ◽  
Yun Hu ◽  
Li Zhi Cai
Keyword(s):  
Cloud Computing ◽  
Test Case ◽  
Test Cases ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Testing Framework ◽  
On Demand ◽  
Software Automation Testing ◽  
Efficient Software ◽  
Automation Testing

With the increasingly fast-paced software releasing or updating, research on the method of an efficient software automation testing framework based on cloud computing has become particularly important. In this paper, we propose an automation testing framework over cloud. We also describe some key technologies in the aspect of the design of hierarchical test case and automatic distribution of test cases in the cloud computing environment. Testing experiments show that our framework can take advantage of on-demand testing resources in the cloud to improve the efficiency of automation testing.

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 Software Test Case Generation and it’s curtail using G-Genetic Algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 852-855
Keyword(s):  
Software Testing ◽  
Optimization Problem ◽  
Information Age ◽  
Software Test ◽  
Test Case ◽  
Test Cases ◽  
Show Business ◽  
Np Complete ◽  
The Mean ◽  
The Ideal

Project is a collection of similar activities that are going to be executed in certain order. Among the phases of project management testing show business crucial role. The intension of testing is not to prove the correctness; it is the process of verifying and validation. Software Testing is the most challenging job among all the peers of the industry. Exhaustive software Testing is never possible only Optimized software testing is possible. Hence Software Testing can be viewed as optimization problem as it fall under NP complete. Because of the extensive number of experiments that are required to perform adequate testing of the ideal programming application; the different strategies to decrease the test suite is required. One of the normal contemplated strategies is evacuating the repetitive experiments; the reason is insignificant number of experiments and greatest number of mistakes seclusion or revealing. In this exploration work consider is directed to address the usage and viability of G-hereditary calculation so as to decrease the quantity of experiments that don't included unmistakable incentive in the mean of test inclusion or where the experiments can't separate blunders. Hereditary calculation is used in this work to help in limiting the experiments or streamlining the experiments, where the hereditary calculation creates the primer populace arbitrarily, computes the wellness esteem utilizing inclusion measurements, and after that particular the posterity in back to back ages utilizing hereditary tasks choice, traverse and transformation. The hereditary displaying activities are explicit and dependent on the task may fluctuate to ordinary Genetic calculation. This procedure of age is rehashed until there is no adjustment in the wellness esteems for two successive ages, when there is no adjustment in the information age for two emphases so union accomplished or a minimized test case is achieved. The results of study demonstrate that, genetic algorithms can significantly reduce the size of the test cases

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