• International Journal of Contents
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• v.13 no.1
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• pp.38-44
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• 2017

Increased use of software and complexity of software functions, as well as shortened software quality evaluation periods, have increased the importance and necessity for automation of software testing. Automating software testing by using machine learning not only minimizes errors in manual testing, but also allows a speedier evaluation. Research on machine learning in automated software testing has so far focused on solving special problems with algorithms, leading to difficulties for the software developers and testers, in applying machine learning to software testing automation. This paper, proposes a new machine learning framework for software testing automation through related studies. To maximize the performance of software testing, we analyzed and categorized the machine learning algorithms applicable to each software test phase, including the diverse data that can be used in the algorithms. We believe that our framework allows software developers or testers to choose a machine learning algorithm suitable for their purpose.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.2
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• pp.309-323
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• 2007

Recently, E-Learning based on wide-area infrastructure is being spotlighted as the new means to innovate education at school and develop human resources at society and appeared as the main point of digital content industry. In this paper, we analyze the characteristics of base technology of E-Learning software and developed E-Learning software quality evaluation model by analyzing quality characteristics for quality test and evaluation of E-Learning software. To do so, we established the quality evaluation system and developed the evaluation model to evaluate the quality about E-Learning software by introducing related international standard. We think that this will promote development of competitive E-Learning software products.

• The Journal of Korean Association of Computer Education
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• v.22 no.1
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• pp.31-46
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• 2019

The purpose of this study is to analyze the self-directed learning types of software education learners and to characterize them according to each type. To do this, 429 middle school students participating in online software education at K university were surveyed and a latent class analysis to analyze self-directed learning types was conducted. As a result, the self-directed learning types of the software education learners were classified into 'highest level of self-directed learning type (class 1)', 'self learning style recognition type (class 2)', 'self learning style preference type (class 3)', and 'lack of self-directed learning type(class 4)'. Also, the level of software learning achievement according to self-directed learning type of software education learners was found to be the highest at 'highest level of self-directed learning type (class 1)' and lowest at 'self learning style preference type (class 3)'. Based on these results, we suggested the strategic implications for software education.

• Journal of The Korean Association of Information Education
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• v.20 no.5
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• pp.433-442
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• 2016

The purpose of this paper is to prove the effect of flipped learning on learning motivation in software education. For this study, the experiment was consisted of experimental group and control group for university of education. They learned software education using Scratch programming for 10 weeks. The experimental group was applied to the flipped learning with online software courses. As a result, post motivation in the experimental group was significantly increased compared to the pre learning motivation. Post- learning motivation is also the experimental group were higher learning motivation significantly compared to the control group. It is hoped this flipped learning as an effective learning strategies base expansion in primary and secondary education as well as high education teaching software in the future.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.258-266
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• 2024

E-learning systems have proliferated in recent years, particularly in the wake of the global COVID-19 pandemic. For kids, there isn't a specific online learning platform available, though. To do this, new conceptual models of training and learning software that are adapted to the abilities and preferences of end users must be created. Young pupils: those in kindergarten, preschool, and elementary school are unique subjects with little research history. From the standpoint of software technology, young students who have never had access to a computer system are regarded as specific users with high expectations for the functionality and interface of the software, social network connectivity, and instantaneous Internet communication. In this study, we suggested creating an electronic learning management system that is web-based and appropriate for primary school pupils. User-centered design is the fundamental technique that was applied in the development of the system that we are proposing. Test findings have demonstrated that students who are using the digital environment for the first time are studying more effectively thanks to the online learning management system.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.104-110
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• 2020

In the 4th Industrial Revolution, the competitiveness of the software industry is important, and as a solution to fundamentally secure the competitiveness of the software industry, education classes should be provided to educate high quality software personnel in educational institutions. Despite this social situation, software-related classes in universities are largely composed of competitive or individual learning structures. Cooperative learning is a learning model that can complement the problems of competitive and individual learning. Cooperative learning is more effective in improving academic achievement than individual or competitive learning. In addition, most learners have the advantage of having a more desirable self-image by having a successful experience. In this paper, we apply a pair check model, which is a type of cooperative learning, in operating system classes. In addition, the class procedure and instruction plan are designed to apply the pair check model. We analyze the test results to analyze the performance of the cooperative learning model.

• Journal of KIISE
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• v.43 no.12
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• pp.1325-1333
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• 2016

Software classification has several applications such as copyright infringement detection, malware classification, and software automatic categorization in software repositories. It can be also employed by software filtering systems to prevent the transmission of illegal software. If illegal software is identified by measuring software similarity in software filtering systems, the average number of comparisons can be reduced by shrinking the search space. In this study, we focused on the classification of Windows executables using API call information and machine learning. We evaluated the classification performance of machine learning-based classifier according to the refinement method for API information and machine learning algorithm. The results showed that the classification success rate of SVM (Support Vector Machine) with PolyKernel was higher than other algorithms. Since the API call information can be extracted from binary executables and machine learning-based classifier can identify tampered executables, API call information and machine learning-based software classifiers are suitable for software filtering systems.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.2
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• pp.1-10
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• 2012

In this study, software products developed in the course of testing, software managers in the process of testing software test and test tools for effective learning effects perspective has been studied using the NHPP software. The finite failure nonhomogeneous Poisson process models presented and the life distribution applied exponential and log shaped type hazard function. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than autonomous errors-detected factor that is generally efficient model could be confirmed. This paper, a failure data analysis of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection were efficient using the mean square error and coefficient of determination.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.2
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• pp.1-8
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• 2011

In this study, software products developed in the course of testing, software managers in the process of testing software test and test tools for effective learning effects perspective has been studied using the NHPP software. The finite failure non-homogeneous Poisson process models presented and the life distribution applied extreme distribution which used to find the minimum (or the maximum) of a number of samples of various distributions. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than automatic error that is generally efficient model could be confirmed. This paper, a numerical example of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection were efficient using the mean square error.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.861-880
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• 2023

Semantic segmentation of road scene is the key technology of autonomous driving, and the improvement of convolutional neural network architecture promotes the improvement of model segmentation performance. The existing convolutional neural network has the simplification of learning knowledge and the complexity of the model. To address this issue, we proposed a road scene semantic segmentation algorithm based on multi-task collaborative learning. Firstly, a depthwise separable convolution atrous spatial pyramid pooling is proposed to reduce model complexity. Secondly, a collaborative learning framework is proposed involved with saliency detection, and the joint loss function is defined using homoscedastic uncertainty to meet the new learning model. Experiments are conducted on the road and nature scenes datasets. The proposed method achieves 70.94% and 64.90% mIoU on Cityscapes and PASCAL VOC 2012 datasets, respectively. Qualitatively, Compared to methods with excellent performance, the method proposed in this paper has significant advantages in the segmentation of fine targets and boundaries.