Multi-criteria test cases selection for model transformations

2020 ◽  
Vol 27 (1-2) ◽  
pp. 91-118
Author(s):  
Bader Alkhazi ◽  
Chaima Abid ◽  
Marouane Kessentini ◽  
Dorian Leroy ◽  
Manuel Wimmer
Keyword(s):  
Model Transformations ◽  
Test Cases ◽  
Selection For

Test Pair Selection for Test Case Prioritization in Regression Testing for WS-BPEL Programs

2013 ◽  
Vol 10 (1) ◽  
pp. 73-102 ◽  
Author(s):  
Lijun Mei ◽  
Yan Cai ◽  
Changjiang Jia ◽  
Bo Jiang ◽  
W.K. Chan
Keyword(s):  
Web Services ◽  
Structural Similarity ◽  
Regression Testing ◽  
Test Pair ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Empirical Results ◽  
Selection For

Many web services not only communicate through XML-based messages, but also may dynamically modify their behaviors by applying different interpretations on XML messages through updating the associated XML Schemas or XML-based interface specifications. Such artifacts are usually complex, allowing XML-based messages conforming to these specifications structurally complex. Testing should cost-effectively cover all scenarios. Test case prioritization is a dimension of regression testing that assures a program from unintended modifications by reordering the test cases within a test suite. However, many existing test case prioritization techniques for regression testing treat test cases of different complexity generically. In this paper, the authors exploit the insights on the structural similarity of XML-based artifacts between test cases in both static and dynamic dimensions, and propose a family of test case prioritization techniques that selects pairs of test case without replacement in turn. To the best of their knowledge, it is the first test case prioritization proposal that selects test case pairs for prioritization. The authors validate their techniques by a suite of benchmarks. The empirical results show that when incorporating all dimensions, some members of our technique family can be more effective than conventional coverage-based techniques.

Evolutionary selection for regression test cases based on diversity

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Baoying Ma ◽  
Li Wan ◽  
Nianmin Yao ◽  
Shuping Fan ◽  
Yan Zhang
Keyword(s):  
Test Cases ◽  
Evolutionary Selection ◽  
Regression Test ◽  
Selection For

scholarly journals Coverage-Based Test Cases Selection for XACML Policies

2014 ◽  
Author(s):  
Antonia Bertolino ◽  
Yves Le Traon ◽  
Francesca Lonetti ◽  
Eda Marchetti ◽  
Tejeddine Mouelhi
Keyword(s):  
Test Cases ◽  
Selection For

scholarly journals Fixing Multiple Type Errors in Model Transformations with Alternative Oracles to Test Cases.

2021 ◽  
Vol 20 (3) ◽  
pp. 9:1
Author(s):  
Paula Muñoz ◽  
Priyanka Karkhanis ◽  
Mark van den Brand
Keyword(s):  
Model Transformations ◽  
Test Cases ◽  
Multiple Type

Regression Test Cases selection for Object-Oriented Programs based on Affected Statements

2015 ◽  
Vol 9 (10) ◽  
pp. 91-108 ◽  
Author(s):  
Samaila Musa ◽  
Abu Bakar Md Sultan ◽  
Abdul Azim Bin Abd-Ghani ◽  
Salmi Baharom
Keyword(s):  
Object Oriented ◽  
Test Cases ◽  
Regression Test ◽  
Selection For

Input Selection for Modeling and Diagnostics With Application to Diesel Engines

2006 ◽  
Vol 129 (1) ◽  
pp. 114-120 ◽  
Author(s):  
Brett Martin ◽  
Peter Meckl
Keyword(s):  
Conditional Entropy ◽  
Diagnostic Algorithm ◽  
Good Choice ◽  
Test Cases ◽  
Algorithm Performance ◽  
Input Selection ◽  
Information Theoretic ◽  
Input Space ◽  
Selection For ◽  
Diagnostic Applications

A theoretical and experimental approach to the use of information theory in input space selection for modeling and diagnostic applications is examined. The assumptions and test cases used throughout the paper are specifically tailored to diesel engine diagnostic and modeling applications. This work seeks to quantify the amount of information about an output contained within an input space. The information theoretic quantity, conditional entropy, is shown to be an accurate predictor of model and diagnostic algorithm performance and therefore is a good choice for an input vector selection metric. Methods of estimating conditional entropy from collected data, including the amount of needed data, are also discussed.

scholarly journals Test Case Prioritization & Selection for an Object Oriented Software using Genetic Algorithm.

2020 ◽  
Vol 9 (4) ◽  
pp. 349-354
Keyword(s):  
Genetic Algorithm ◽  
Object Oriented ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Test Case Selection ◽  
Case Selection ◽  
Selection For ◽  
The Given

In this paper our aim is to propose a Test Case Selection and Prioritization technique for OOP for ordering the test cases as per in accordance with their priority for finding the faults in the OOS. We have used the heuristic Genetic Algorithm, in order to generating the order of these prioritized test cases for a given OOS. The motive is to put a test case first into the ordered sequence that may have the highest prospective of finding an error in the given OOS & then soon..

scholarly journals Evolutionary Algorithm to Support Field Architecture Scenario Screening Automation and Optimization Using Decentralized Subsea Processing Modules

Processes ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 184
Author(s):  
Mariana J. C. Díaz Arias ◽  
Allyne M. dos Santos ◽  
Edmary Altamiranda
Keyword(s):  
Thermal Management ◽  
Concept Development ◽  
Test Cases ◽  
Production Network ◽  
Concept Selection ◽  
Genetic Operators ◽  
Number Of Solutions ◽  
Field Development ◽  
Variation Operators ◽  
Selection For

Manual generation of test cases and scenario screening processes, during field architecture concept development, may produce a limited number of solutions that do not necessarily lead to an optimal concept selection. For more complex subsea field architectures, which might include processing modules for enhancing pressure and thermal management for the production network, the number of configuration cases and scenarios to evaluate can be extremely large and time and resource-consuming to handle through conventional manual design processes. This paper explores the use of evolutionary algorithms (EA) to automate case generation, scenario screening, and optimization of decentralized subsea processing modules during field development. An evaluation of various genetic operators and evolution strategies was performed to compare their performance and suitability to the application. Based on the evaluation results, an EA using structural uniform crossover and a gradient plus boundary mutation as the main variation operators was developed. The methodology combines EA and an integrated modeling approach to automate and optimize the concept selection and field architecture design when considering decentralized subsea processing modules.

Linear Visco-Resistive Computations of Magnetohydrodynamic Waves: I. The Code and Test Cases

1994 ◽  
Vol 144 ◽  
pp. 503-505
Author(s):  
R. Erdélyi ◽  
M. Goossens ◽  
S. Poedts
Keyword(s):  
Resonant Absorption ◽  
Numerical Code ◽  
Mhd Waves ◽  
Test Cases ◽  
Numerical Accuracy ◽  
Element Discretization ◽  
Flux Tubes ◽  
Magnetohydrodynamic Waves ◽  
Viscosity Coefficients ◽  
Magnetic Flux Tubes

AbstractThe stationary state of resonant absorption of linear, MHD waves in cylindrical magnetic flux tubes is studied in viscous, compressible MHD with a numerical code using finite element discretization. The full viscosity tensor with the five viscosity coefficients as given by Braginskii is included in the analysis. Our computations reproduce the absorption rates obtained by Lou in scalar viscous MHD and Goossens and Poedts in resistive MHD, which guarantee the numerical accuracy of the tensorial viscous MHD code.

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