• Journal of KIISE
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• v.42 no.11
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• pp.1386-1396
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• 2015

Graphical user interface testing (GUI testing) techniques have been widely used to test the functionality of Android applications (apps) and to detect faults for verification of the reliability and usability of apps. To adequately test the behaviors of apps, a number of studies on model-based GUI testing techniques have been performed on Android apps. However, the effectiveness of model-based techniques greatly depends on the quality of the GUI model, because model-based GUI testing techniques generate test inputs based on this model. Therefore, in order to improve testing effectiveness in model-based techniques, accurate and efficient GUI model generation has to be achieved using an improved model generation technique with concrete definition of GUI states. For accurate and efficient generation of a GUI model and test inputs, this study suggests a hierarchical GUI state comparison technique and evaluates this technique through comparison with the existing model-based techniques, considering activities as GUI states. Our results show that the proposed technique outperforms existing approaches and has the potential to improve the performance of model-based GUI testing techniques for Android apps.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.553-560
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• 2001

Recently, ultrasonic testing simulation has becomes very important in the field of nondestructive evaluation due to its unique capability of providing testing signals without real inspection. The ultrasonic testing simulation requires three elementary models including the transducer beam radiation model, the flaw scattering model, and the reception model. In the present work, we briefly describe an approach to develop the ultrasonic testing model together with its elementary models with the multi-gaussian beam model. Based on this approach, we developed ultrasonic testing simulation program with MATLAB. The performance of the developed program is demonstrated by the predicting of ultrasonic signals from two types of flaws, circulars crack and spheres.

• Journal of KIISE
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• v.43 no.9
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• pp.953-962
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• 2016

Model-based testing is a technique for performing the test by using a model that represents the behavior of the system as a system specification. Industrial domains such as automotive, military/aerospace, medical, railway and nuclear power generation require model-based testing and code coverage-based testing to improve the quality of software. Despite the fact that both model-based testing and code coverage-based testing are required, difficulty in achieving a high coverage using model-based testing caused by the abstraction level difference between the test model and the source code, results in the need for performing model-based testing separately. In this study, to overcome the limitations of the existing model-based testing, we proposed the state machine transformation method to effectively improve the code coverage using the protocol state machine, one of the typical modeling methods is used as the test model in model-based testing, as the test model. In addition, we performed a case study of both systems and analyzed the effectiveness of the proposed method.

• Journal of information and communication convergence engineering
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• v.6 no.4
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• pp.448-453
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• 2008

This study describes design and development techniques of estimation models for process modeling. One case study is undertaken to design a model using standard gas furnace data. Neural networks (NN) and genetic programming (GP) are each employed to model the crucial relationships between input factors and output responses. In the case study, two models were generated by using 70% training data and evaluated by using 30% testing data for genetic programming and neural network modeling. The model performance was compared by using RMSE values, which were calculated based on the model outputs. The average RMSE for training and testing were 0.8925 (training) and 0.9951 (testing) for the NN model, and 0.707227 (training) and 0.673150 (testing) for the GP model, respectively. As concern the results, the NN model has a strong advantage in model training (using the all data for training), and the GP model appears to have an advantage in model testing (using the separated data for training and testing). The performance reproducibility of the GP model is good, so this approach appears suitable for modeling physical fabrication processes.

• International Journal of Reliability and Applications
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• v.8 no.1
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• pp.53-66
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• 2007

This paper proposes a new non-homogeneous Poisson process software reliability growth model based on the coverage information. The new model incorporates the coverage information in the fault detection process by assuming that only the faults in the covered constructs are detectable. Since the coverage growth behavior depends on the testing strategy, the fault detection process is first modeled for the general testing strategy and then realized for the uniform testing. Finally the model for the uniform testing is empirically evaluated by applying it to real data sets.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.607-620
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• 2018

The problem surrounding methods of implementing the software testing process has come under the spotlight in recent times. However, as compliance with the software testing process does not necessarily bring with it immediate economic benefits, IT companies need to pursue more aggressive efforts to improve the process, and the software industry needs to makes every effort to improve the software testing process by evaluating the Test Maturity Model integration (TMMi). Furthermore, as the software test process is only at the initial level, high-quality software cannot be guaranteed. This paper applies TMMi model to Automobile control software testing process, including test policy and strategy, test planning, test monitoring and control, test design and execution, and test environment goal. The results suggest improvement of the automobile control software testing process based on Test maturity model. As a result, this study suggest IT organization's test process improve method.

• Journal of Information Technology Applications and Management
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• v.12 no.2
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• pp.1-13
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• 2005

This paper proposes a software-reliability growth model incoporating the amount of testing effort expended during the software testing phase after developing it. The time-dependent behavior of testing effort expenditures is described by a Logistic curve. Assuming that the error detection rate to the amount of testing effort spent during the testing phase is proportional to the current error content, a software-reliability growth model is formulated by a nonhomogeneous Poisson process. Using this model the method of data analysis for software reliability measurement is developed. After defining a software reliability, This paper discusses the relations between testing time and reliability and between duration following failure fixing and reliability are studied. SRGM in several literatures has used the exponential curve, Railleigh curve or Weibull curve as an amount of testing effort during software testing phase. However, it might not be appropriate to represent the consumption curve for testing effort by one of already proposed curves in some software development environments. Therefore, this paper shows that a logistic testing-effort function can be adequately expressed as a software development/testing effort curve and that it gives a good predictive capability based on real failure data.

• Smart Structures and Systems
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• v.14 no.6
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• pp.1105-1129
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• 2014

The unscented Kalman filter (UKF) has been developed for nonlinear model parametric identification, and it assumes that the model parameters are symmetrically distributed about their mean values without any constrains. However, the parameters in many applications are confined within certain ranges to make sense physically. In this paper, a constrained unscented Kalman filter (CUKF) algorithm is proposed to improve accuracy of numerical substructure modeling in hybrid testing. During hybrid testing, the numerical models of numerical substructures which are assumed identical to the physical substructures are updated online with the CUKF approach based on the measurement data from physical substructures. The CUKF method adopts sigma points (i.e., sample points) projecting strategy, with which the positions and weights of sigma points violating constraints are modified. The effectiveness of the proposed hybrid testing method is verified by pure numerical simulation and real-time as well as slower hybrid tests with nonlinear specimens. The results show that the new method has better accuracy compared to conventional hybrid testing with fixed numerical model and hybrid testing based on model updating with UKF.

• Journal of Korean Society for Quality Management
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• v.29 no.4
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• pp.92-102
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• 2001

Software testing during development and operation should exercise to obtain the desired software quality and leave failure data set. So far, many software reliability models are classified and can be used to measure a software reliability only based on its failure history But, in practice, developers or testers of software systems must decide which existing software reliability model can be fitted. In this paper, we will show that an appropriate reliability model can be selected by considering relations between characteristics of each testing environment and models' assumptions. Several methods of software testing are presented and discussed. Also, unit test, integrated test, function test and system test that are sequentially exercised during development will be introduced.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.513-532
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• 2022

In this paper, the use of Bayes factors and the deviance information criterion for model selection are compared in a Bayesian accelerated life testing setup. In Bayesian accelerated life testing, the most used tool for model comparison is the deviance information criterion. An alternative and more formal approach is to use Bayes factors to compare models. However, Bayesian accelerated life testing models with more than one stressor often have mathematically intractable posterior distributions and Markov chain Monte Carlo methods are employed to obtain posterior samples to base inference on. The computation of the marginal likelihood is challenging when working with such complex models. In this paper, methods for approximating the marginal likelihood and the application thereof in the accelerated life testing paradigm are explored for dual-stress models. A simulation study is also included, where Bayes factors using the different approximation methods and the deviance information are compared.