Using Genetic Algorithms to Repair JUnit Test Cases

2014 ◽  
Author(s):  
Yong Xu ◽  
Bo Huang ◽  
Guoqing Wu ◽  
Mengting Yuan
Keyword(s):  
Genetic Algorithms ◽  
Test Cases

scholarly journals Software Test Data Generation based on Path Testing using Genetic Algorithms

2020 ◽  
Vol 8 (6) ◽  
pp. 4466-4473
Keyword(s):  
Genetic Algorithms ◽  
Test Data ◽  
Iterative Algorithms ◽  
Continuous Functions ◽  
Software Test ◽  
Test Cases ◽  
Test Data Generation ◽  
Data Generation ◽  
Optimal Solutions ◽  
Automated Test

Test data generation is the task of constructing test cases for predicting the acceptability of novel or updated software. Test data could be the original test suite taken from previous run or imitation data generated afresh specifically for this purpose. The simplest way of generating test data is done randomly but such test cases may not be competent enough in detecting all defects and bugs. In contrast, test cases can also be generated automatically and this has a number of advantages over the conventional manual method. Genetic Algorithms, one of the automation techniques, are iterative algorithms and apply basic operations repeatedly in greed for optimal solutions or in this case, test data. By finding out the most error-prone path using such test cases one can reduce the software development cost and improve the testing efficiency. During the evolution process such algorithms pass on the better traits to the next generations and when applied to generations of software test data they produce test cases that are closer to optimal solutions. Most of the automated test data generators developed so far work well only for continuous functions. In this study, we have used Genetic Algorithms to develop a tool and named it TG-GA (Test Data Generation using Genetic Algorithms) that searches for test data in a discontinuous space. The goal of the work is to analyze the effectiveness of Genetic Algorithms in automated test data generation and to compare its performance over random sampling particularly for discontinuous spaces.

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 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.

scholarly journals Optimization Method of Web Fuzzy Test Cases Based on Genetic Algorithm

2021 ◽  
Vol 2078 (1) ◽  
pp. 012015
Author(s):  
Sheng Qu ◽  
Zheng Zhang ◽  
Bolin Ma ◽  
Yuwen Shao
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Web Service ◽  
Web Applications ◽  
Optimization Method ◽  
Test Cases ◽  
Public Network ◽  
Code Coverage ◽  
Signature Database ◽  
The Web

Abstract In order to solve the problems of low code coverage, few vulnerabilities found, and poor fuzzing effect caused by the small number of test cases and single types in Web fuzzing, on the basis of studying the current Web fuzzing methods, the existing fuzzing Web applications are tested Program research. A genetic algorithm-based method for optimizing fuzzing test cases for Web applications is proposed. It analyzes and counts the traffic of public network website business with Web service attack characteristics, and uses genetic algorithms to generate a large number of test cases with various types to explore the Web service vulnerability that exists. Based on the creation of a Web attack signature database with weights, this method uses genetic algorithms to randomly pre-generate the test cases of the fuzzing test, and uses the response of the Web service to repeatedly iterate the weights of different attack signatures in the Web attack signature database. So as to generate the best test cases. Experimental analysis shows that this method effectively finds security vulnerabilities in Web applications.

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