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 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 Critical Review on Automated Test Case Generation for Conducting Combinatorial Testing Using Particle Swarm Optimization

2018 ◽  
Vol 7 (3.8) ◽  
pp. 22 ◽  
Author(s):  
Dr V. Chandra Prakash ◽  
Subhash Tatale ◽  
Vrushali Kondhalkar ◽  
Laxmi Bewoor
Keyword(s):  
Particle Swarm Optimization ◽  
Critical Review ◽  
Optimization Problem ◽  
Test Suite ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Combinatorial Testing ◽  
Swarm Optimization ◽  
Automated Test Case Generation

In software development life cycle, testing plays the significant role to verify requirement specification, analysis, design, coding and to estimate the reliability of software system. A test manager can write a set of test cases manually for the smaller software systems. However, for the extensive software system, normally the size of test suite is large, and the test suite is prone to an error committed like omissions of important test cases, duplication of some test cases and contradicting test cases etc. When test cases are generated automatically by a tool in an intelligent way, test errors can be eliminated. In addition, it is even possible to reduce the size of test suite and thereby to decrease the cost & time of software testing.It is a challenging job to reduce test suite size. When there are interacting inputs of Software under Test (SUT), combinatorial testing is highly essential to ensure higher reliability from 72 % to 91 % or even more than that. A meta-heuristic algorithm like Particle Swarm Optimization (PSO) solves optimization problem of automated combinatorial test case generation. Many authors have contributed in the field of combinatorial test case generation using PSO algorithms.We have reviewed some important research papers on automated test case generation for combinatorial testing using PSO. This paper provides a critical review of use of PSO and its variants for solving the classical optimization problem of automatic test case generation for conducting combinatorial testing.   

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.

scholarly journals Smart Test Case Quantifier Based on Mc/Dc Coverage Criterion with Uml Sequence Diagram

2020 ◽  
Vol 9 (1) ◽  
pp. 1266-1278
Keyword(s):  
Software Testing ◽  
Initial Phase ◽  
Black Box ◽  
Sequence Diagram ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Maximum Coverage ◽  
Coverage Criterion ◽  
White Box Testing

Software testing is an inevitable phase in the Software Development Life Cycle (SDLC). It plays a vital role in ensuring the quality of the product. Testing can be performed on requirement, design and code. Traditional test case generation approaches majorly focuses on designing and implementation models, as a huge percentage of errors in the software are caused due to the lack of understanding in the early phases. So, it’s advisory to follow testing in the initial phase in SDLC as most of the errors/faults can be eliminated and can be prevented without disseminating to the next phase. So, testing must not be secluded to a single phase alone in SDLC. In software testing, test case generation is the fundamental and challenging part. The major purpose to design test case is to create set of tests that are valuable in validation. Due to the increase in software size, it’s not quite advisable to have the choice as manual testing to test the code as its error prone, complex and time consuming. Automation of testing would help the tester to test effectively and timely. Applications of new techniques improve the test process and cut down the tester’s effort. The proposed work presents a sandwich of black box and white box testing. Design-based testing, a black box approach is implemented, that aids in fixing the errors at initial phase. Unified Modeling Language (UML) Use case diagram, Activity diagram and Sequence Diagram are considered for designing. The code-based generation of test cases, is a white box testing approach, test coverage criteria Modified Condition/Decision Condition (MC/DC) has been used to ensure the maximum coverage of the code during testing phase for generating the test cases. The objective of this paper is to automate the generation of minimal number of test cases required to test a system with maximum coverage by removing the redundant test cases using MC/DC criterion. This present an idea to the beginners of the testing about minimal test cases they necessitate for testing their system.

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.

scholarly journals Pengembangan sistem otomatisasi pembangkitan kasus uji dengan algoritma genetika dan test case generation method

Teknologi ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
Moh Arsyad Mubarak Setyawan ◽  
Fajar Pradana ◽  
Bayu Priyambadha
Keyword(s):  
Software Testing ◽  
Directed Graph ◽  
Control Flow ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Unit Testing ◽  
Control Flow Graph ◽  
Population Number ◽  
Flow Graph

Pengujian perangkat lunak merupakan salah satu bagian penting dari pembuatan perangkat lunak. Pada pengujian perangkat lunak terdapat pengujian unit. Pengujian unit merupakan proses pengujian komponen yang berfokus untuk memverifikasi unit terkecil pada perancangan perangkat lunak. Pada tahap pengujian unit terdapat proses pembangkitan kasus uji. Selama ini, pembangkitan kasus uji dari suatu kode program dilakukan secara manual se-hingga membutuhkan waktu yang lama. Hal ini dikarenakan banyaknya kemungkinan jalur pada kode sumber yang akan diuji.  Dalam penelitian ini dibangun suatu sistem otomatis untuk membangkitkan kasus uji. Alur kerja sistem dimulai dari analisa kode sumber dengan Spoon Library, selanjutnya dibentuk CFG (Control Flow Graph) dan DDG (Dynamic Directed Graph). Dari DDG tersebut akan dibangkitkan jalur layak yang terdapat pada DDG, dengan menggunakan algoritma genetika diharapkan dapat mengoptimalkan penentuan jalur independen. Dari masing-masing jalur independen akan dibangkitkan kasus ujinya dengan metode test case generation. Pengujian akurasi sistem pada sistem otomatisasi pembangkit kasus uji dengan jumlah populasi 5, 10 dan 15 serta jumlah maksimum generasi 50, 100, 200 dan 250 dihasilkan jumlah populasi paling optimal yaitu 10 dan maksimum generasi optimal yaitu 200 dengan akurasi 93,33%. Pada jumlah populasi dan maksimum generasi sesudahnya tidak terjadi peningkatan akurasi yang signifikan. Tiap peningkatan jumlah populasi dan maksimum generasi dapat meningkatkan akurasi sistem.  Software testing is one of the most important part of making software. On the software testing there are unit testing. Unit Testing is a process for verifying component, focusing on the smallest unit of software design. In the unit testing phase contained test case generation process. During this time, the generation of test cases of a program code is done manually. In this study, constructed an automated system to generate test cases. The workflow system starts from the analysis of the source code with the library spoon and then create CFG (Control Flow Graph) and DDG (Dynamic Directed graph). From the DDG will be raised feasible path using a genetic algorithm. Furthermore, from fea-sible path sought independenth path which is a path base d on the level of uniqueness of the path to the other path. From each independenth path raised the test case with a test case generation method. Testing accuracy of the system on the automation system generating test cases with populations of 5,10 and 15 as well as the maximum number of generations 50, 100, 200 and 250 produced the most optimal population number is 15 and the most optimal maximum generation is 200 with accuracy 93.33%. Each increase in the number of population and maximum generation can improve the accuracy of the system. Level accuracy with population number over 10 and maximum generation over 200 has no increace accuracy significant.

An Approach to Generate Test Cases by Multi-Path Based on Genetic Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3976-3979
Author(s):  
Yu Liu ◽  
Feng Qin Wang ◽  
Xiu Li Zhao
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Software Testing ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Test Environment ◽  
Quality And Reliability ◽  
Classification Procedures

Software testing is important to ensure the quality and reliability of the software.The improvement on the automation of test case generation is the entire key to improve the automation of the testing process.It helps a lot in the generation of test cases to construct multi-path model.It is based on genetic algorithm with three parts which are the test environment construction, the genetic algorithms and the operating environment.It’s feasibility and efficiency is verified by triangle classification procedures.

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