scholarly journals Forest Optimization based Test Case Generation for Multiple Paths with Metamorphic Relations

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Efficient Method ◽  
Optimization Algorithm ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Initial Test ◽  
Time Consumption ◽  
Path Coverage ◽  
Multiple Paths ◽  
Metaheuristic Techniques

In general multiple paths are covered by multiple runs which is a time consuming task. Now a days, metaheuristic techniques are widely used for path coverage. In order to reduce the time, an efficient method is proposed based on Forest Optimization Algorithm (FOA) with Metamorphic Relations (MRs) that cover multiple paths at a time in one run unlike the traditional search based testing. In the proposed approach, initial test case is generated using FOA, the successive test cases are generated using MRs without undergoing several runs. The motive of using FOA is that the searching mechanism of this algorithm having resemblance with the branch / path coverage techniques of testing. To the best of our knowledge, FOA has not been implemented in software testing. The experimental results are compared with three existing work. The efficiency of simply FOA is also shown how it able to cover multiple paths. The results show that FOA with MRs is more efficient in terms of time consumption and number of paths covered.

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.

Metaheuristic Techniques for Test Case Generation

2022 ◽  
pp. 1043-1058
Author(s):  
Rashmi Rekha Sahoo ◽  
Mitrabinda Ray
Keyword(s):  
Software Testing ◽  
Selection Procedure ◽  
Primary Objective ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Metaheuristic Techniques

The primary objective of software testing is to locate bugs as many as possible in software by using an optimum set of test cases. Optimum set of test cases are obtained by selection procedure which can be viewed as an optimization problem. So metaheuristic optimizing (searching) techniques have been immensely used to automate software testing task. The application of metaheuristic searching techniques in software testing is termed as Search Based Testing. Non-redundant, reliable and optimized test cases can be generated by the search based testing with less effort and time. This article presents a systematic review on several meta heuristic techniques like Genetic Algorithms, Particle Swarm optimization, Ant Colony Optimization, Bee Colony optimization, Cuckoo Searches, Tabu Searches and some modified version of these algorithms used for test case generation. The authors also provide one framework, showing the advantages, limitations and future scope or gap of these research works which will help in further research on these works.

Metaheuristic Techniques for Test Case Generation

2018 ◽  
Vol 11 (1) ◽  
pp. 158-171 ◽  
Author(s):  
Rashmi Rekha Sahoo ◽  
Mitrabinda Ray
Keyword(s):  
Software Testing ◽  
Optimization Problem ◽  
Selection Procedure ◽  
Primary Objective ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Metaheuristic Techniques

The primary objective of software testing is to locate bugs as many as possible in software by using an optimum set of test cases. Optimum set of test cases are obtained by selection procedure which can be viewed as an optimization problem. So metaheuristic optimizing (searching) techniques have been immensely used to automate software testing task. The application of metaheuristic searching techniques in software testing is termed as Search Based Testing. Non-redundant, reliable and optimized test cases can be generated by the search based testing with less effort and time. This article presents a systematic review on several meta heuristic techniques like Genetic Algorithms, Particle Swarm optimization, Ant Colony Optimization, Bee Colony optimization, Cuckoo Searches, Tabu Searches and some modified version of these algorithms used for test case generation. The authors also provide one framework, showing the advantages, limitations and future scope or gap of these research works which will help in further research on these works.

scholarly journals A Proposal for Automatic Testing of GUIs Based on Annotated Use Cases

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
Pedro Luis Mateo Navarro ◽  
Diego Sevilla Ruiz ◽  
Gregorio Martínez Pérez
Keyword(s):  
Automatic Testing ◽  
Use Cases ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Generation Process ◽  
Test Coverage ◽  
Initial Test ◽  
New Approach ◽  
Coverage Analysis

This paper presents a new approach to automatically generate GUI test cases and validation points from a set of annotated use cases. This technique helps to reduce the effort required in GUI modeling and test coverage analysis during the software testing process. The test case generation process described in this paper is initially guided by use cases describing the GUI behavior, recorded as a set of interactions with the GUI elements (e.g., widgets being clicked, data input, etc.). These use cases (modeled as a set of initial test cases) are annotated by the tester to indicate interesting variations in widget values (ranges, valid or invalid values) and validation rules with expected results. Once the use cases are annotated, this approach uses the new defined values and validation rules to automatically generate new test cases and validation points, easily expanding the test coverage. Also, the process allows narrowing the GUI model testing to precisely identify the set of GUI elements, interactions, and values the tester is interested in.

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.

scholarly journals Bi-Objective Constraint and Hybrid Optimizer for the Test Case Prioritization

2019 ◽  
Vol 8 (6) ◽  
pp. 3436-3448
Keyword(s):  
Taylor Series ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Optimal Test ◽  
Average Percentage ◽  
Branch Coverage

Regression testing is performed to make conformity that any changes in software program do not disturb the existing characteristics of the software. As the software improves, the test case tends to grow in size that makes it very costly to be executed, and thus the test cases are needed to be prioritized to select the effective test cases for software testing. In this paper, a test case prioritization technique in regression testing is proposed using a novel optimization algorithm known as Taylor series-based Jaya Optimization Algorithm (Taylor-JOA), which is the integration of Taylor series in Jaya Optimization Algorithm (JOA). The optimal test cases are selected based on the fitness function, modelled depending on the constraints, namely fault detection and branch coverage. The experimentation of the proposed Taylor-JOA is performed with the consideration of the evaluation metrics, namely Average Percentage of Fault Detected (APFD) and the Average Percentage of Branch Coverage (APBC). The APFD and the APBC of the proposed Taylor-JOA is 0.995, and 0.9917, respectively, which is high as compared to the existing methods that show the effectiveness of the proposed Taylor-JOA in the task of test case prioritization

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.

Optimized Test Case Generation for Object Oriented Systems Using Weka Open Source Software

2021 ◽  
pp. 596-618
Author(s):  
Rajvir Singh ◽  
Anita Singhrova ◽  
Rajesh Bhatia
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Effective Means ◽  
Object Oriented ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Time Saving ◽  
Class Level ◽  
Fault Proneness

Detection of fault proneness classes helps software testers to generate effective class level test cases. In this article, a novel technique is presented for an optimized test case generation for ant-1.7 open source software. Class level object oriented (OO) metrics are considered as effective means to find fault proneness classes. The open source software ant-1.7 is considered for the evaluation of proposed techniques as a case study. The proposed mathematical model is the first of its kind generated using Weka open source software to select effective OO metrics. Effective and ineffective OO metrics are identified using feature selection techniques for generating test cases to cover fault proneness classes. In this methodology, only effective metrics are considered for assigning weights to test paths. The results indicate that the proposed methodology is effective and efficient as the average fault exposition potential of generated test cases is 90.16% and test cases execution time saving is 45.11%.

Sign in / Sign up

Export Citation Format

Share Document