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 Software Test Data Generation based on Path Testing using Genetic Algorithms

2020 ◽  
Vol 8 (6) ◽  
pp. 4466-4473
Keyword(s):  
Genetic Algorithms ◽  
Test Data ◽  
Iterative Algorithms ◽  
Continuous Functions ◽  
Software Test ◽  
Test Cases ◽  
Test Data Generation ◽  
Data Generation ◽  
Optimal Solutions ◽  
Automated Test

Test data generation is the task of constructing test cases for predicting the acceptability of novel or updated software. Test data could be the original test suite taken from previous run or imitation data generated afresh specifically for this purpose. The simplest way of generating test data is done randomly but such test cases may not be competent enough in detecting all defects and bugs. In contrast, test cases can also be generated automatically and this has a number of advantages over the conventional manual method. Genetic Algorithms, one of the automation techniques, are iterative algorithms and apply basic operations repeatedly in greed for optimal solutions or in this case, test data. By finding out the most error-prone path using such test cases one can reduce the software development cost and improve the testing efficiency. During the evolution process such algorithms pass on the better traits to the next generations and when applied to generations of software test data they produce test cases that are closer to optimal solutions. Most of the automated test data generators developed so far work well only for continuous functions. In this study, we have used Genetic Algorithms to develop a tool and named it TG-GA (Test Data Generation using Genetic Algorithms) that searches for test data in a discontinuous space. The goal of the work is to analyze the effectiveness of Genetic Algorithms in automated test data generation and to compare its performance over random sampling particularly for discontinuous spaces.

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.

scholarly journals An Approach for Robust System Testing the Event Data Recorder in the Context of a Passive Safety System

Vehicles ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 426-447
Author(s):  
Alexander Strassheim
Keyword(s):  
Test Data ◽  
Control Unit ◽  
Safety System ◽  
Passive Safety ◽  
Test Design ◽  
Event Data ◽  
System Testing ◽  
Test Execution ◽  
Automated Test ◽  
The Common

As long as road accidents happen, passive safety systems like the airbag control unit are an essential part of the whole automotive safety system. Within the airbag control unit, the event data recorder (EDR) is an integrated function. Recent developments in legislation show that an increasing number of EDR-related regulations are being introduced. They are mainly focusing on data recording, crash-data retrieval, and some of them define testing aspects. In the system testing of an airbag control unit with a focus on the event data recorder, the question arises of how to deal with the fact that real-world crash events are not “straightforward” but arbitrary and do not follow any rules and restrictions. The purpose of this work is to develop a robust test approach to these conditions—giving a tester the possibility to extend the test depth considering the common test design techniques and testing principles. The applied methodology is the use of optimization algorithms in an automated test environment. With this, the tester can steer the test execution in a predefined way with minimal interaction. The application of the developed test method automatically creates a set of test data which fulfill the predefined conditions by the user. The generated results show that a high number of test data are created at and close to the target condition. Consequently, this test approach provides an extension to the common test design techniques with regard to how test input data can be created, and especially how automated test data creation and test execution can be realized.

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.

The Research on Test Automation Based on the Window Application Software

2011 ◽  
Vol 135-136 ◽  
pp. 1093-1095
Author(s):  
Jian Ping Ren ◽  
Mei Hong Zhao
Keyword(s):  
Automatic Generation ◽  
Sql Server ◽  
Test Case ◽  
Test Cases ◽  
Test Model ◽  
Test Automation ◽  
Test Analysis ◽  
Structure Information ◽  
Windows Applications ◽  
Software Information

In order to implement software automated test on the application of .NET form class, in this paper, we propose a program that builds resources platforms for under test programs, just like software structure information store and common window widgets test case library. It takes white-box method for the test model, with the use of SQL Server databases. The program executes cover tests on those common interaction widgets easy to result in exceptions for Windows applications. At the same time, it gets attributes information of the window widgets. Then it achieves automatic generation of test cases, and imports them into Excel. Based on the related software information generated above, the program will complete the test automatically, and give the test analysis report to improve the efficiency of software testing.

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.

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.

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).

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