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Generating effective test suites for model transformations using classifying terms

Loli Burgueño, Frank Hilken, Antonio Vallecillo, Martin Gogolla
2016 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems  
This paper explores the use of classifying terms and stratified sampling for developing richer test cases for model transformations.  ...  Generating sample models for testing a model transformation is no easy task.  ...  Once the classifying terms are defined for a tract, the USE tool generates one representative model for each equivalence class. These canonical models constitute the test suite of the tract.  ... 
dblp:conf/models/BurguenoHVG16 fatcat:x5ntcdcwjfbt3mk6g6jvu5lvji

Employing classifying terms for testing model transformations

Martin Gogolla, Antonio Vallecillo, Loli Burgueno, Frank Hilken
2015 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS)  
The technique is applied for automatically constructing relevant source model test cases for model transformations between a source and target metamodel.  ...  A classifying term can be an arbitrary OCL term on the class model that calculates for an object model a characteristic value.  ...  One essential aspect of model transformation testing (and, in general, of software testing) is the selection of effective test cases [1] .  ... 
doi:10.1109/models.2015.7338262 dblp:conf/models/GogollaVBH15 fatcat:ydy72tdaezh5fmr7yec3innwna

Testing Code Generators: a Case Study on Applying USE, EFinder and Tracts in Practice

Zijun Chen, Wilbert Alberts, Ivan Kurtev
2021 International Conference on Software Technologies: Applications and Foundations  
We focused on two aspects: automatic support for generating an efficient suite of input test models and alleviating the test oracle problem by using lightweight transformation specifications based on the  ...  A commonly found application of model transformations is in the implementation of code generators where typically a chain of model-to-model and model-to-text transformations is used.  ...  Conclusions We presented initial results from a case study of testing an industrial code generator that uses a combination of M2M and M2T transformations.  ... 
dblp:conf/staf/ChenAK21 fatcat:tvm7eykt7fa7ji7fwps6n344ju

Controlled Evaluation of Grammatical Knowledge in Mandarin Chinese Language Models [article]

Yiwen Wang, Jennifer Hu, Roger Levy, Peng Qian
2021 arXiv   pre-print
We train LSTMs, Recurrent Neural Network Grammars, Transformer language models, and Transformer-parameterized generative parsing models on two Mandarin Chinese datasets of different sizes.  ...  Prior work has shown that structural supervision helps English language models learn generalizations about syntactic phenomena such as subject-verb agreement.  ...  Across test suite classes, the Transformer-Xinhua models outperform their smaller CTB counterparts (p < .05), but the effect of data size is less clear for the LSTM, RNNG and PLM models.  ... 
arXiv:2109.11058v1 fatcat:hywgm7hlfnbcxiqub47dapkh3q

Model transformation testing

Gehan M. K. Selim, James R. Cordy, Juergen Dingel
2012 Proceedings of the First Workshop on the Analysis of Model Transformations - AMT '12  
In this paper, we survey the model transformation testing phases and the approaches proposed in the literature for each phase.  ...  Transformation testing is however different from testing code, and presents new challenges.  ...  Using the generated criteria and an initial test suite, a footprint was generated for each test model. A footprint is a vector of the number of times a test model covers each criterion.  ... 
doi:10.1145/2432497.2432502 dblp:conf/models/SelimCD12 fatcat:tmlvvi355ndt7ezzpbb4wbwska

GUI Testing Techniques: A Survey

Imran Ali Qureshi, Aamer Nadeem
2013 International Journal of Future Computer and Communication  
We have classified the GUI test case generation techniques on the basis of Fault Models which is a novel work in the context of GUI testing.  ...  Our Analysis shows a clear picture about the usefulness of each technique.  ...  The generated test case suite includes inputs, expected outputs and necessary infrastructure to execute the tests automatically. Basically this approach uses data model to generate test cases.  ... 
doi:10.7763/ijfcc.2013.v2.139 fatcat:plknz6yzsra4ro2mv2bo5binha

Reliable Mining of Automatically Generated Test Cases from Software Requirements Specification (SRS) [article]

Lilly Raamesh, G. V. Uma
2010 arXiv   pre-print
And also the paper presents a method for reduction of test suite by using mining methods thereby facilitating the mining and knowledge extraction from test cases.  ...  This is systematically transformed into state charts considering all relevant information. The current paper outlines how test cases can be automatically generated from these state charts.  ...  Generation of Test Cases From State Machines to Test Cases Using state models to derive test cases has been common practice in the software testing world for some time .The final goal of model-based  ... 
arXiv:1002.1199v1 fatcat:odty64xphfaflp74zlajlo7x5m

Automated Product Line Methodologies to Support Model-Based Testing

Shuai Wang, Shaukat Ali, Arnaud Gotlieb
2013 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems  
Cost-effective testing products can be divided into three main problems, i.e., test selection, test generation, and test minimization.  ...  Testing products in a cost-efficient way remains an attractive topic for Model-Based Testing (MBT) of product lines in both academia and industry, which can be addressed by employing systematic and automated  ...  Finally, the configured models will be given as in input to a tool called TRansformation-based tool for Uml-baSed Testing (TRUST) for generating executable test cases used to test the SUT system through  ... 
dblp:conf/models/WangAG13 fatcat:6r3sj6hfm5e3zhz7bxywnpqjki

Supporting component selection with a suite of classifiers

Valerie Maxville, Chiou Peng Lam, Jocelyn Armarego
2008 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)  
The application of the suite is illustrated using a selection and evaluation case study.  ...  This paper describes the CdCE process and our approach to assist the shortlisting of candidates through a suite of classifiers.  ...  The ideal specification is used as a model to generate training data for two machine learning classifiers to shortlist and later to evaluate the available components.  ... 
doi:10.1109/cec.2008.4631334 dblp:conf/cec/MaxvilleLA08 fatcat:drnxw64wafcipdb7dhiyzaijda

Learning of somatosensory representations for texture discrimination using a temporal coherence principle

Joerg Hipp, Wolfgang Einhäuser, Jörg Conradt, Peter König
2005 Network  
To account for the rat's active sensing behaviour, we record whisker movements in a hardware model.  ...  Using a simple, unsupervised classifier, the performance on the output cell population is same as if using a sophisticated supervised classifier on the primary cells.  ...  In order to analyse the responses of a single cell in velocity-position space, we generate test stimuli akin to the filters used for primary cells.  ... 
doi:10.1080/09548980500361582 pmid:16411497 fatcat:xtmrjh2jcrcwnocn5r554dws7u

An EFSM-Based Test Data Generation Approach in Model-Based Testing

Muhammad Luqman Mohd-Shafie, Wan Mohd Nasir Wan Kadir, Muhammad Khatibsyarbini, Mohd Adham Isa, Israr Ghani, Husni Ruslai
2022 Computers Materials & Continua  
Model-based testing (MBT) is a technique that uses system models to generate and execute test cases automatically.  ...  It was identified that the test data generation (TDG) in many existing model-based test case generation (MB-TCG) approaches were still manual.  ...  Acknowledgement: The authors would like to express their deepest gratitude to the Software Engineering Research Group (SERG) members and the anonymous reviewers for their constructive comments and suggestions  ... 
doi:10.32604/cmc.2022.023803 fatcat:4wbvz3r4rfbcjl6s5xlszxph4q

Texture classification based on the Generalized Gamma distribution and the Dual Tree Complex Wavelet Transform

Ahmed Drissi El Maliani, Mohammed El Hassouni, Nouredine Lasmar, Yannick Berthoumieu
2010 2010 5th International Symposium On I/V Communications and Mobile Network  
This paper deals with stochastic texture modeling for classification issue. A generic stochastic model based on three-parameter Generalized Gamma (GG) distribution function is proposed.  ...  The GG modeling offers more flexibility parameterization than other kinds of heavy-tailed density devoted to wavelet empirical histograms characterization.  ...  The interpretation and use of the GG modeling are discussed, and we present a texture classifier using both the GGD model and the GG one.  ... 
doi:10.1109/isvc.2010.5656257 fatcat:snk5s66r3rbnvpemapdw7rl4ee

Classification Trends Taxonomy of Model-based Testing for Software Product Line: A Systematic Literature Review

2022 KSII Transactions on Internet and Information Systems  
The multiple types of measurement required a trade-off between maximization and minimization results to ensure the testing method which could satisfy multiple test criteria for example cost and effectiveness  ...  Objective: The objective of this study is to analyze and classify trends of MBT in SPL covering the solutions, issues and evaluation aspects by using taxonomy form.  ...  Coverage is frequently used in MBT for SPL as it is often used as an indicator to measure the effectiveness of the test suite.  ... 
doi:10.3837/tiis.2022.05.008 fatcat:zpw7ofnytbftriq5zejwfcnm2q

A Novel Scheme to Classify EHG Signal for Term and Pre-term Pregnancy Analysis

Sindhiya Arora, Girisha Garg
2012 International Journal of Computer Applications  
Early prediction of premature pregnancy reduces neonatal death and helps in adoption of treatment well suited for the pre-term pregnancy state.  ...  There are scads of work done in the area of term and pre-term pregnancy analysis like artificial intelligence, regressive models, and higher order statistical models.  ...  The classifier is first trained using the training set and then tested using the test set.  ... 
doi:10.5120/8144-1928 fatcat:3w2szdr4p5gh7hz75imzn5x3za

A comparison of SVM and RVM for Document Classification [article]

Muhammad Rafi, Mohammad Shahid Shaikh
2013 arXiv   pre-print
SVM is a supervised machine learning technique that can be used for classification task.  ...  On the other hand RVM uses probabilistic measure to define this separation space. RVM uses Bayesian inference to obtain succinct solution, thus RVM uses significantly fewer basis functions.  ...  We have used micro-average and macro-average F measure. For evaluating data set we have used 10 fold cross-validations and used paired t-test to assess the significance.  ... 
arXiv:1301.2785v1 fatcat:shco57hj7jdrzlqspz5dekkwva
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