Automatic Generation of Test Cases from Formal Specifications using Mutation Testing

2021 ◽  
Author(s):  
Roman Jaramillo Cajica ◽  
Raul Ernesto Gonzalez Torres ◽  
Pedro Mejia Alvarez
Keyword(s):  
Automatic Generation ◽  
Mutation Testing ◽  
Test Cases ◽  
Formal Specifications

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.

AUTOMATIC GENERATION OF TEST CASES FROM ACTIVITY DIAGRAMS FOR UML BASED TESTING (UBT)

2015 ◽  
Vol 77 (13) ◽  
Author(s):  
Oluwatolani Oluwagbemi ◽  
Hishammuddin Asmuni
Keyword(s):  
Automatic Generation ◽  
Test Cases ◽  
System Testing ◽  
Activity Diagrams ◽  
Model File ◽  
Transition Criteria

Activity diagrams are one of UML behavioural models suitable for system testing because it has the capacity to effectively describe the behaviours of systems under development. In this paper, a technique is proposed that generates test cases from activity diagrams by constructing an activity flow tree (AFT) which stores all the information extracted from the model file of the diagram through the help of a parser. Then, we applied an algorithm to generate test cases from the constructed tree. Test cases were generated based on the elements of activity diagrams such as activity sequences, associated descriptions and conditions. The proposed technique generated accurate test cases that completely tallied with the modeled requirements in the diagram. We utilized all-paths, basic pair paths, conditions, branches and transition criteria for generating test cases using ATM withdrawal operation software as a case study.

GENERATING TEST CASES FROM FORMAL SPECIFICATIONS

1996 ◽  
Vol 6 (1) ◽  
pp. 463-470 ◽  
Author(s):  
Kalman C. Toth ◽  
Michael R. Donat ◽  
Jeffrey J. Joyce
Keyword(s):  
Test Cases ◽  
Formal Specifications

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.

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