Software test case generation based on the fault propagation path coverage

2016 ◽  
Author(s):  
Wang Kun ◽  
Wang Yichen
Keyword(s):  
Propagation Path ◽  
Software Test ◽  
Test Case Generation ◽  
Test Case ◽  
Fault Propagation ◽  
Path Coverage

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 Generation for UML Behavioral Diagram by Traversal Algorithm

2019 ◽  
Vol 8 (10) ◽  
pp. 3262-3266
Keyword(s):  
Accurate Result ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Sequence Diagrams ◽  
Research Focus ◽  
Activity Diagram ◽  
Path Coverage ◽  
The Comparative Study ◽  
Traversal Algorithm

Software testing play crucial role in the software development as it consumes lot of time and resources. However testing process needs to be more efficiently done because overall software quality relies upon good testing approach. The present research focus on generation of test cases from UML diagrams. The combination graph is made by using activity and sequence diagrams. These diagrams proves to be more efficient as activity diagram gives the dynamic behavior of the model and sequence diagram is used to understand detailed functionality of the system. In this paper, a combined approach using Breadth first and depth first search is proposed which will generate expected test cases. The comparative study is done for test case generation using BFS and DFS algorithm and the result proves that the DFS traversal algorithm provides more accurate result for path coverage.

Automatic Software Test Case Generation

2011 ◽  
Vol 5 (3) ◽  
pp. 91-101 ◽  
Author(s):  
M.R. Keyvanpour ◽  
H. Homayouni ◽  
Hasein Shirazee
Keyword(s):  
Software Test ◽  
Test Case Generation ◽  
Test Case ◽  
Automatic Software

Research on Automated Software Test Case Generation

2014 ◽  
Vol 9 (11) ◽  
Author(s):  
Daisen Wei ◽  
Longye Tang ◽  
Xueqing Li ◽  
Ling Shang
Keyword(s):  
Software Test ◽  
Test Case Generation ◽  
Test Case ◽  
Automated Software

Framework for Reusable Test Case Generation in Software Systems Testing

2020 ◽  
pp. 212-229
Author(s):  
Kamalendu Pal
Keyword(s):  
Software Development ◽  
Software Testing ◽  
Software Test ◽  
Test Case Generation ◽  
Software Systems ◽  
Test Case ◽  
Case Based Reasoning ◽  
Test Cases ◽  
Development Teams ◽  
Functional Software

Agile methodologies have become the preferred choice for modern software development. These methods focus on iterative and incremental development, where both requirements and solutions develop through collaboration among cross-functional software development teams. The success of a software system is based on the quality result of each stage of development with proper test practice. A software test ontology should represent the required software test knowledge in the context of the software tester. Reusing test cases is an effective way to improve the testing of software. The workload of a software tester for test-case generation can be improved, previous software testing experience can be shared, and test efficiency can be increased by automating software testing. In this chapter, the authors introduce a software testing framework (STF) that uses rule-based reasoning (RBR), case-based reasoning (CBR), and ontology-based semantic similarity assessment to retrieve the test cases from the case library. Finally, experimental results are used to illustrate some of the features of the framework.

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