A Controlled Experiment on Coverage Maximization of Automated Model-Based Software Test Cases in the Automotive Industry

2017 ◽  
Author(s):  
Rashid Darwish ◽  
Lynnie Nakyanzi Gwosuta ◽  
Richard Torkar
Keyword(s):  
Automotive Industry ◽  
Controlled Experiment ◽  
Software Test ◽  
Test Cases ◽  
Model Based ◽  
Coverage Maximization

Optimization of a Shock Absorber Design Using Model-Based Approach

2012 ◽  
Vol 452-453 ◽  
pp. 1351-1355 ◽  
Author(s):  
Grzegorz Wszołek ◽  
Piotr Czop ◽  
Dawid Jakubowski ◽  
Damian Slawik
Keyword(s):  
Automotive Industry ◽  
Shock Absorber ◽  
Flow Characteristic ◽  
Optimization Method ◽  
Design Parameters ◽  
Coupled Models ◽  
Model Based ◽  
Noise Evaluation ◽  
Optimal Values ◽  
Application Scope

The aim of this paper is to demonstrate a possibility to optimize a shock absorber design to minimize level of vibrations with the use of model-based approach. The paper introduces a proposal of an optimization method that allows to choose the optimal values of the design parameters using a shock absorber model to minimize the level of vibrations. A model-based approach is considered to obtain the optimal pressure-flow characteristic by simulations conducted with the use of coupled models, including the damper and the servo-hydraulic tester model. The presence of the tester model is required due to high non-linear coupling of the tested object (damper) and the tester itself to be used for noise evaluation. This kind of evaluation is used in the automotive industry to investigate dampers, as an alternative to vehicle-level tests. The paper provides numerical experimental case studies to show application scope of the proposed method

scholarly journals Towards an Automated Model-Based Test Generation Approach from Controlled Natural Language Specifications Written in Arabic

2021 ◽  
Author(s):  
Moez Krichen ◽  
Seifeddine Mechti
Keyword(s):  
Natural Language ◽  
Test Generation ◽  
Arabic Language ◽  
Test Cases ◽  
New Model ◽  
Controlled Natural Language ◽  
Model Based ◽  
Case Grammar ◽  
Model Based Testing ◽  
Testing Approach

<div>We propose a new model-based testing approach which takes as input a set of requirements described in Arabic Controlled Natural Language (CNL) which is a subset of the Arabic language generated by a specific grammar. The semantics of the considered requirements is defined using the Case Grammar Theory (CTG). The requirements are translated into Transition Relations which serve as an input for test cases generation tools.</div>

scholarly journals Towards an Automated Model-Based Test Generation Approach from Controlled Natural Language Specifications Written in Arabic

2021 ◽  
Author(s):  
Moez Krichen ◽  
Seifeddine Mechti
Keyword(s):  
Natural Language ◽  
Test Generation ◽  
Arabic Language ◽  
Test Cases ◽  
New Model ◽  
Controlled Natural Language ◽  
Model Based ◽  
Case Grammar ◽  
Model Based Testing ◽  
Testing Approach

<div>We propose a new model-based testing approach which takes as input a set of requirements described in Arabic Controlled Natural Language (CNL) which is a subset of the Arabic language generated by a specific grammar. The semantics of the considered requirements is defined using the Case Grammar Theory (CTG). The requirements are translated into Transition Relations which serve as an input for test cases generation tools.</div>

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.

MBTDD: MODEL BASED TEST DRIVEN DEVELOPMENT

2012 ◽  
Vol 22 (08) ◽  
pp. 1085-1102 ◽  
Author(s):  
ALIREZA SADEGHI ◽  
SEYED-HASSAN MIRIAN-HOSSEINABADI
Keyword(s):  
Human Resource Management ◽  
Human Resource ◽  
Systems Development ◽  
Test Cases ◽  
Use Case ◽  
Improved Method ◽  
Test Driven Development ◽  
Writing Test ◽  
Model Based ◽  
Model Based Testing

Test Driven Development (TDD), as a quality promotion approach, suffers from some shortages that discourage its usage. One of the most challenging shortcomings of TDD is the low level of granularity and abstraction. This may lead to production of software that is not acceptable by the end users. Additionally, exploiting of TDD is not applicable in the enterprise systems development. To overcome this defect, we have merged TDD with Model Based Testing (MBT) and suggested a framework named Model Based Test Driven Development (MBTDD). According to TDD, writing test cases comes before programming, and based on our improved method of TDD, modeling precedes writing test cases. To validate the applicability of the proposed framework, we have implemented a use case of Human Resource Management (HRM) system by means of MBTDD. The empirical results of using MBTTD show that our proposed method overwhelms existing deficiencies of TDD.

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