Experience from SIMMAN 2008—The First Workshop on Verification and Validation of Ship Maneuvering Simulation Methods

2011 ◽  
Vol 55 (02) ◽  
pp. 135-147 ◽  
Author(s):  
F. Stern ◽  
K. Agdraup ◽  
S. Y. Kim ◽  
A. C. Hochbaum ◽  
K. P. Rhee ◽  
...  
Keyword(s):  
Test Data ◽  
Model Test ◽  
Simulation Method ◽  
Test Case ◽  
Test Cases ◽  
Simulation Methods ◽  
Ship Maneuvering ◽  
Comparison Results ◽  
Wide Range ◽  
Surface Combatant

The SIMMAN 2008 workshop was held in Copenhagen, Denmark in April 2008. The purpose of the workshop was to benchmark the prediction capabilities of different ship maneuvering simulation methods including systems- and CFD-based methods through systematic quantitative comparisons and validation against EFD data for tanker (KVLCC), container ship (KCS), and surface combatant (5415) hull form test cases. For the KVLCC test case, two stern shape variants named KVLCC1 and KVLCC2 giving different instability loops were included. Free model test data was compared with systems-based methods and CFD for specified free maneuvers. Some of the systems-based methods used provided PMM and CMT data, and two used CFD instead. CFD-based methods were used to simulate forced motions and were compared with PMM/CMT model test data. The submissions were blind in the sense that the benchmark model test data was not provided prior to the workshop, unless data was required as input to the simulation method. A total of 64 submissions were received for the free maneuver simulations, which included a wide range of the state-of-the-art methods in use today, such as PMM- and CMT-based methods, CFD based methods, system identification, neural network tools and various empirical methods. For the forced motion simulations a total of 16 submissions were received, comprising different CFD-based methods such as RANS, URANS, and DES. This paper gives an overview of hulls, model tests, test cases, submissions, comparison results as well as the most important observations and conclusions.

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 PEMBANGKITAN DATA UJI BERBASIS DIAGRAM AKTIVITAS DAN DIAGRAM STATECHART

JOUTICA ◽  
2018 ◽  
Vol 3 (1) ◽  
pp. 137
Author(s):  
Arif Rahman Sujatmika ◽  
Yanuangga Gala Hartlambang
Keyword(s):  
Test Data ◽  
Test Case ◽  
Test Cases ◽  
Data Dictionary ◽  
Activity Diagram ◽  
Statechart Diagram ◽  
The Status ◽  
Generate Test Data ◽  
Case Data ◽  
Selection Of

Testing is the stage of software development used to determine whether a software is ready for release or not. In making test cases using reference activity diagrams and statechart diagrams, a help representation was made, ie State-Activity-Diagram (SAD). The generation of test cases using a reference between the statechart diagram and the status diagram is still inadequate because in the case of the test produced there is no test data. The selection of test data for many test cases will be tedious and time consuming. In this paper, it is proposed to generate test data automatically based on existing test cases. Test data created based on class diagrams, and data dictionaries. The test case data consists of inputs and results. First enter information about the functions involved in the test case into the SAD node so that the SAD-S Diagram is obtained. Second, after the process of making the test case is completed, the test data is made by looking at the data dictionary function so that the test data is formed.

USLTG: Test Case Automatic Generation by Transforming Use Cases

2019 ◽  
Vol 29 (09) ◽  
pp. 1313-1345 ◽  
Author(s):  
Chu Thi Minh Hue ◽  
Duc-Hanh Dang ◽  
Nguyen Ngoc Binh ◽  
Anh-Hoang Truong
Keyword(s):  
Test Data ◽  
Test Generation ◽  
Automatic Generation ◽  
Specification Language ◽  
Use Cases ◽  
Test Case ◽  
Test Cases ◽  
Generate Functional ◽  
Build System ◽  
Test Objects

This paper proposes a transformation-based method to automatically generate functional test cases from use cases named USLTG (Use case Specification Language (USL)-based Test Generation). We first focus on developing a modeling language named Test Case Specification Language (TCSL) in order to express test cases. Test cases in TCSL can contain detailed information including test steps, test objects within steps, actions of test objects, and test data. Such information is often ignored in currently available test case specifications. We then aim to generate test cases in a TCSL model by a transformation from use cases that are represented by a USL. The USLTG transformation includes three main steps in generating (1) scenarios, (2) test data, and (3) a TCSL model. Within our transformation, the OCL solver is employed in order to build system snapshots as the part of test cases and to identify other test data. We applied our method to two case studies and evaluated our method by comparing it with other recent works.

scholarly journals Automated Test Data Generation Based on a Genetic Algorithm with Maximum Code Coverage and Population Diversity

2021 ◽  
Vol 11 (10) ◽  
pp. 4673
Author(s):  
Tatiana Avdeenko ◽  
Konstantin Serdyukov
Keyword(s):  
Test Data ◽  
Fitness Function ◽  
Population Diversity ◽  
Test Case ◽  
Test Cases ◽  
Data Generation ◽  
Intelligent Support ◽  
Automated Generation ◽  
Code Coverage ◽  
Automated Test Data Generation

In the present paper, we investigate an approach to intelligent support of the software white-box testing process based on an evolutionary paradigm. As a part of this approach, we solve the urgent problem of automated generation of the optimal set of test data that provides maximum statement coverage of the code when it is used in the testing process. We propose the formulation of a fitness function containing two terms, and, accordingly, two versions for implementing genetic algorithms (GA). The first term of the fitness function is responsible for the complexity of the code statements executed on the path generated by the current individual test case (current set of statements). The second term formulates the maximum possible difference between the current set of statements and the set of statements covered by the remaining test cases in the population. Using only the first term does not make it possible to obtain 100 percent statement coverage by generated test cases in one population, and therefore implies repeated launch of the GA with changed weights of the code statements which requires recompiling the code under the test. By using both terms of the proposed fitness function, we obtain maximum statement coverage and population diversity in one launch of the GA. Optimal relation between the two terms of fitness function was obtained for two very different programs under testing.

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).

Guided Test Case Generation for Enhanced ECG Bio-Sensors Functional Verification

2017 ◽  
Vol 8 (4) ◽  
pp. 1-20
Author(s):  
Hussam Al Hamadi ◽  
Amjad Gawanmeh ◽  
Mahmoud Al-Qutayri
Keyword(s):  
Primary Objective ◽  
Sensor Nodes ◽  
Functional Verification ◽  
Test Case ◽  
Test Cases ◽  
Correct Operation ◽  
Ecg Signals ◽  
Wide Range ◽  
Formal Design ◽  
Electrocardiogram Ecg

Testing and verifying the operation of bio-sensor nodes is essential due to the sensitivity and safety-critical aspects of their applications. Simulation technique is frequently used for this task; however, a proper set of test cases is required in order to carry out the simulation process. This paper focuses on enhancing the verification operations of an ElectroCardioGram (ECG) biomedical sensor node through simulation. It presents a new methodology for guided Test Cases Generation (TCG) of ECG signals from formal design specifications. Event-B invariants are used to specify ECG requirements, and then a new algorithm is used to translate these specifications into proper ECG signal parameters. These parameters are subsequently used to control the required shape of the ECG in order to have a wide range of scenarios. The primary objective of this work is to provide ECG test cases to detect design errors in biomedical algorithms. In addition, it can complement the usage of the limited ECG databases currently available to verify the correct operation of ECG bio-sensors.

scholarly journals On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1145 ◽  
Author(s):  
Shweta Rani ◽  
Bharti Suri ◽  
Rinkaj Goyal
Keyword(s):  
Genetic Algorithm ◽  
Test Data ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Data Generation ◽  
Data Generation ◽  
Test Execution ◽  
Mutation Strategy

Manual test case generation is an exhaustive and time-consuming process. However, automated test data generation may reduce the efforts and assist in creating an adequate test suite embracing predefined goals. The quality of a test suite depends on its fault-finding behavior. Mutants have been widely accepted for simulating the artificial faults that behave similarly to realistic ones for test data generation. In prior studies, the use of search-based techniques has been extensively reported to enhance the quality of test suites. Symmetry, however, can have a detrimental impact on the dynamics of a search-based algorithm, whose performance strongly depends on breaking the “symmetry” of search space by the evolving population. This study implements an elitist Genetic Algorithm (GA) with an improved fitness function to expose maximum faults while also minimizing the cost of testing by generating less complex and asymmetric test cases. It uses the selective mutation strategy to create low-cost artificial faults that result in a lesser number of redundant and equivalent mutants. For evolution, reproduction operator selection is repeatedly guided by the traces of test execution and mutant detection that decides whether to diversify or intensify the previous population of test cases. An iterative elimination of redundant test cases further minimizes the size of the test suite. This study uses 14 Java programs of significant sizes to validate the efficacy of the proposed approach in comparison to Initial Random tests and a widely used evolutionary framework in academia, namely Evosuite. Empirically, our approach is found to be more stable with significant improvement in the test case efficiency of the optimized test suite.

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.

A Lightweight-Leveled Software Automated Test Framework

2013 ◽  
Vol 834-836 ◽  
pp. 1919-1924 ◽  
Author(s):  
Jian Ping Zhao ◽  
Xiao Yang Liu ◽  
Hong Ming Xi ◽  
Li Ya Xu ◽  
Jian Hui Zhao ◽  
...  
Keyword(s):  
Test Data ◽  
Data Driven ◽  
Test Design ◽  
Test Case ◽  
Test Cases ◽  
Test Automation ◽  
Test Tool ◽  
Automated Test ◽  
Case Files ◽  
Data Files

To resolve the problem of a large amount of automated test scripts and test data files, through the test tool QTP, data-driven and keyword-driven testing mechanism, a test automation framework based on three layer data-driven mechanism is designed, including the design of the TestSet managing test case files, the design of the TestCase storing test cases and the design of the TestData storing test data.Through controlling the test scale and applying the test data pool, reconfigurable and optimization of test scripts are designed. The methods above can decouple the test design and the script development, make test cases and data show a more humane design, make test scripts and test data on the business level optimized and reusable, and make the number of script files and the test data files reache a minimum, which reduces the occupied space.

scholarly journals Automated Specification-Based Testing of REST APIs

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5375
Author(s):  
Ovidiu Baniaș ◽  
Diana Florea ◽  
Robert Gyalai ◽  
Daniel-Ioan Curiac
Keyword(s):  
Web Applications ◽  
User Interaction ◽  
Automatic Generation ◽  
Test Case ◽  
Test Cases ◽  
Generation Process ◽  
Coverage Metrics ◽  
Representational State Transfer ◽  
Wide Range ◽  
And Performance

Nowadays, REpresentational State Transfer Application Programming Interfaces (REST APIs) are widely used in web applications, hence a plethora of test cases are developed to validate the APIs calls. We propose a solution that automates the generation of test cases for REST APIs based on their specifications. In our approach, apart from the automatic generation of test cases, we provide an option for the user to influence the test case generation process. By adding user interaction, we aim to augment the automatic generation of APIs test cases with human testing expertise and specific context. We use the latest version of OpenAPI 3.x and a wide range of coverage metrics to analyze the functionality and performance of the generated test cases, and non-functional metrics to analyze the performance of the APIs. The experiments proved the effectiveness and practicability of our method.

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