Evolutionary Approaches to Test Data Generation for Object-Oriented Software

2022 ◽  
pp. 884-909
Author(s):  
Ana Filipa Nogueira ◽  
José Carlos Bregieiro Ribeiro ◽  
Francisco Fernández de Vega ◽  
Mário Alberto Zenha-Rela
Keyword(s):  
Test Data ◽  
Relevant Literature ◽  
Object Oriented ◽  
Data Generation ◽  
Open Problems ◽  
Branch Coverage ◽  
Evolutionary Testing ◽  
Generate Test Data ◽  
Tools And Techniques ◽  
Future Work

In object-oriented evolutionary testing, metaheuristics are employed to select or generate test data for object-oriented software. Techniques that analyse program structures are predominant among the panoply of studies available in current literature. For object-oriented evolutionary testing, the common objective is to reach some coverage criteria, usually in the form of statement or branch coverage. This chapter explores, reviews, and contextualizes relevant literature, tools, and techniques in this area, while identifying open problems and setting ground for future work.

Evolutionary Approaches to Test Data Generation for Object-Oriented Software

2018 ◽  
pp. 162-194
Author(s):  
Ana Filipa Nogueira ◽  
José Carlos Bregieiro Ribeiro ◽  
Francisco Fernández de Vega ◽  
Mário Alberto Zenha-Rela
Keyword(s):  
Test Data ◽  
Relevant Literature ◽  
Object Oriented ◽  
Data Generation ◽  
Open Problems ◽  
Branch Coverage ◽  
Evolutionary Testing ◽  
Generate Test Data ◽  
Tools And Techniques ◽  
Future Work

In object-oriented evolutionary testing, metaheuristics are employed to select or generate test data for object-oriented software. Techniques that analyse program structures are predominant among the panoply of studies available in current literature. For object-oriented evolutionary testing, the common objective is to reach some coverage criteria, usually in the form of statement or branch coverage. This chapter explores, reviews, and contextualizes relevant literature, tools, and techniques in this area, while identifying open problems and setting ground for future work.

Object-Oriented Evolutionary Testing

2014 ◽  
Vol 4 (4) ◽  
pp. 15-35 ◽  
Author(s):  
Ana Filipa Nogueira ◽  
José Carlos Bregieiro Ribeiro ◽  
Francisco Fernández de Vega ◽  
Mário Alberto Zenha-Rela
Keyword(s):  
Software Testing ◽  
Test Data ◽  
Relevant Literature ◽  
Object Oriented ◽  
Open Problems ◽  
Evolutionary Testing ◽  
Generate Test Data ◽  
Future Work

In Object-Oriented Evolutionary Testing, metaheuristics are employed to select or generate Test Data for Object-Oriented software. The application of search-based strategies to the Software Testing of Object-Oriented Software is fairly recent and is yet to be investigated comprehensively; this article aims to explore, review and contextualize relevant literature and research in this area, while pinpointing open problems and setting grounds for future work.

Expert system assisted test data generation for software branch coverage

1991 ◽  
Vol 6 (4) ◽  
pp. 279-295
Author(s):  
James H. Cross ◽  
Kai-Hsiung Chang ◽  
W. Homer Carlisle ◽  
David B. Brown
Keyword(s):  
Expert System ◽  
Test Data ◽  
Test Data Generation ◽  
Data Generation ◽  
Branch Coverage

scholarly journals Mutated Specification-Based Test Data Generation with a Genetic Algorithm

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 331
Author(s):  
Rong Wang ◽  
Yuji Sato ◽  
Shaoying Liu
Keyword(s):  
Genetic Algorithm ◽  
Test Data ◽  
Test Cases ◽  
Formal Specifications ◽  
Data Generation ◽  
Testing Methods ◽  
Functional Changes ◽  
Target Program ◽  
Generate Test Data

Specification-based testing methods generate test data without the knowledge of the structure of the program. However, the quality of these test data are not well ensured to detect bugs when non-functional changes are introduced to the program. To generate test data effectively, we propose a new method that combines formal specifications with the genetic algorithm (GA). In this method, formal specifications are reformed by GA in order to be used to generate input values that can kill as many mutants of the target program as possible. Two classic examples are presented to demonstrate how the method works. The result shows that the proposed method can help effectively generate test cases to kill the program mutants, which contributes to the further maintenance of software.

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