scholarly journals Automatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm

2020 ◽  
Vol 30 (1) ◽  
pp. 59-72
Author(s):  
P Lakshminarayana ◽  
T V SureshKumar
Keyword(s):  
Software Development ◽  
Software Testing ◽  
Test Data ◽  
Cuckoo Search ◽  
Optimization Method ◽  
Automatic Generation ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Bee Colony

AbstractSoftware testing is a very important technique to design the faultless software and takes approximately 60% of resources for the software development. It is the process of executing a program or application to detect the software bugs. In software development life cycle, the testing phase takes around 60% of cost and time. Test case generation is a method to identify the test data and satisfy the software testing criteria. Test case generation is a vital concept used in software testing, that can be derived from the user requirements specification. An automatic test case technique determines automatically where the test cases or test data generates utilizing search based optimization method. In this paper, Cuckoo Search and Bee Colony Algorithm (CSBCA) method is used for optimization of test cases and generation of path convergence within minimal execution time. The performance of the proposed CSBCA was compared with the performance of existing methods such as Particle Swarm Optimization (PSO), Cuckoo Search (CS), Bee Colony Algorithm (BCA), and Firefly Algorithm (FA).

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 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.

Research on Automatic Test Case Generation Framework for Java

2014 ◽  
Vol 568-570 ◽  
pp. 1488-1496
Author(s):  
Ming Gang Xu ◽  
Yong Min Mu ◽  
Zhi Hua Zhang ◽  
Ang Liu
Keyword(s):  
Software Testing ◽  
Automatic Generation ◽  
Difficult Problem ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Labor Costs ◽  
Automatic Test ◽  
Automatic Test Case Generation ◽  
Java Program

Automatic test case generation has been a hotspot and a difficult problem in the software testing, Accurately and efficiently generate test cases can improve the efficiency of software testing. Java programs have many characteristics such as encapsulation, inheritance, polymorphism and so on, it is convenient for software design and development, but to bring automated testing some difficulties. This article on the Java program of automatic test case generation method is studied and presents a framework for automatic generation of test cases. With this framework, test case suite will be generated quickly and accurately. Experimental results show that automatic Java test case generation framework can quickly and accurately generate test cases , reduce labor costs and improve efficiency.

scholarly journals An Enhanced Multiview Test Case Generation Technique for Object-oriented Software using Class and Activity Diagrams

2020 ◽  
Vol 9 (4) ◽  
pp. 186-195
Keyword(s):  
Software Development ◽  
Software Testing ◽  
Unified Modeling Language ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Software Faults ◽  
Single View ◽  
Effective Product ◽  
Uml Models

Software testing is one of the vital steps in software development life cycle. Test case generation is the first process in software testing which takes a lot of time, cost and effort to build an effective product from the start. Automatic test case generation is the best way to address this issue and model-based test case generation approach would be suitable for this automation process. One way to generate test cases automatically is by generating test cases from Unified Modeling Language (UML) models. The challenge with the existing test case generation techniques using UML models is that they provide a single view, meaning that the techniques capture a single aspect of the system, such as structural or behavioral but not both. In this paper, we have successfully developed a technique that automatically generates test cases which capture both structural and behavioral views of the system. These test cases can help to discover software faults early in the software development cycle. Finally, we conducted an experiment by comparing our technique with a manual process. The results show that the proposed technique can produce same test cases as manually writing test cases of the same system model but this technique saves a lot of time, effort and cost as well.

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.

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.

Framework for Reusable Test Case Generation in Software Systems Testing

2022 ◽  
pp. 1090-1108
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.

scholarly journals Test Case Generation Process using Soft Computing Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 4824-4831
Keyword(s):  
Software Testing ◽  
Soft Computing ◽  
Test Data ◽  
Important Task ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Generation Process ◽  
Soft Computing Techniques ◽  
Case Development

Software testing is the SDLC's important and most expensive step. Software testing is difficult and time-consuming work requiring a great deal of money for software development. Testing is both an operation that is static and adaptive. Software testing process deals with the creation of test cases, checking and validating either passed or failed test cases. It is unidealistic to check only the discerning parts of the material as a whole at once. It is not possible to test the whole system once, so selected parts of the code are considered for analysis. Since the input space of the Product Under Test (PUT) can be very large, it is important to analyze a representative subset of test cases. During software testing, the most important task is to build appropriate test cases. An effective set of test cases can detect more errors. Software testing always requires high deficiencies. Test cases are constructed using the test data. In the automation of software testing, the important task is to generate test data according to a given level of competence. The improved test data are determined using the test case development methodology and the test data adequacy criterion being applied. For increase the level of automation and performance, these aspects of test case development need to be studied. This paper studies the various test case generation techniques using soft computing techniques like Genetic Algorithm, Artificial Bee colony methods. Further an evaluation criterion for the test case generation process, empirical study of Code Coverage and its importance is discussed.

scholarly journals TEST CASE PRIORTIZATION USING GENETIC ALGORITHM

2014 ◽  
pp. 151-154
Author(s):  
RUCHIKA MALHOTRA ◽  
ABHISHEK BHARADWAJ
Keyword(s):  
Genetic Algorithm ◽  
Software Development ◽  
Software Testing ◽  
Major Part ◽  
Test Case ◽  
Test Cases ◽  
Development Lifecycle ◽  
Software Development Lifecycle ◽  
The One

Software is built by human so it cannot be perfect. So in order to make sure that developed software does not do any unintended thing we have to test every software before launching it in the operational world. Software testing is the major part of software development lifecycle. Testing involves identifying the test cases which can find the errors in the program. Exhaustive testing is not a good idea to follow. It is very difficult and time consuming to perform. In this paper a technique has been proposed to do prioritize test cases according to their capability of finding errors. One which is more likely to find the errors has been assigned a higher priority and the one which is less likely to find the errors in the program has been assigned low priority. It is recommended to execute the test cases according their priority to find the errors.

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