Prioritization of test case scenarios derived from activity diagram using genetic algorithm

2010 ◽  
Author(s):  
Sangeeta Sabharwal ◽  
Ritu Sibal ◽  
Chayanika Sharma
Keyword(s):  
Genetic Algorithm ◽  
Test Case ◽  
Activity Diagram

Effective Generation of Test Case Based on Genetic Algorithm

2013 ◽  
Vol 321-324 ◽  
pp. 2952-2955
Author(s):  
Zhi Guo Shi ◽  
Li Ren Zou ◽  
Da Peng Tong ◽  
Ming Qian Wang
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Unified Modeling Language ◽  
Test Case ◽  
Activity Diagram ◽  
Unified Modeling ◽  
Component Software ◽  
Overall Design ◽  
Testing Method ◽  
Generating Method

With people pay more and more attention to component software testing, the generation of test case is as one of the important works of software testing, it becomes a hotspot inevitably. For component software testing, making combination of the testing method based on model and testing method based on Genetic Algorithm, this paper proposes a generating method of test case which based on UML (Unified Modeling Language) activity diagram and Genetic Algorithm, and gives the overall design and implementation steps of the method. Then this paper applies the proposed method to model the adding table business in catering management system, and design test case. The example verifies the feasibility and validity of the method.

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.

A Test Case Prioritization Genetic Algorithm Guided by the Hypervolume Indicator

2020 ◽  
Vol 46 (6) ◽  
pp. 674-696 ◽  
Author(s):  
Dario Di Nucci ◽  
Annibale Panichella ◽  
Andy Zaidman ◽  
Andrea De Lucia
Keyword(s):  
Genetic Algorithm ◽  
Test Case ◽  
Test Case Prioritization ◽  
Hypervolume Indicator

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.

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