• Journal of Information Processing Systems
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• v.15 no.4
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• pp.967-985
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• 2019

Bug report processing is a key element of bug fixing in modern software maintenance. Bug reports are not processed immediately after submission and involve several processes such as bug report deduplication and bug report triage before bug fixing is initiated; however, this method of bug fixing is very inefficient because all these processes are performed manually. Software engineers have persistently highlighted the need to automate these processes, and as a result, many automation techniques have been proposed for bug report processing; however, the accuracy of the existing methods is not satisfactory. Therefore, this study focuses on surveying to improve the accuracy of existing techniques for bug report processing. Reviews of each method proposed in this study consist of a description, used techniques, experiments, and comparison results. The results of this study indicate that research in the field of bug deduplication still lacks and therefore requires numerous studies that integrate clustering and natural language processing. This study further indicates that although all studies in the field of triage are based on machine learning, results of studies on deep learning are still insufficient.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.250-260
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• 2018

In order to control the quadcopter using reinforcement learning, hovering of 1-axial drones prototype is implemented through reinforcement learning. A complementary filter is used to measure the correct angle, and the range of angles is from -180 degrees to +180 degrees using modified complementary filter. The policy gradient method is used together with the REINFORCE algorithm for reinforcement learning. The prototype learned in this way confirmed the difference in performance depending on the length of the episode.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.5
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• pp.496-502
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• 2016

In this study, we investigated the comparative NHPP software model based on learning techniques that operators in the process of software testing and development of software products that can be applied to software test tool. The life distribution was applied Makeham distribution based on finite fault NHPP. 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 larger than automatic error that is usually well-organized model could be established. This paper, a trust characterization of applying using time among failures and parameter approximation using maximum likelihood estimation, after the effectiveness of the data through trend examination model selection were well-organized using the mean square error and $R^2$. From this paper, the software operators must be considered life distribution by the basic knowledge of the software to confirm failure modes which may be helped.

• Journal of The Korean Association of Information Education
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• v.21 no.5
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• pp.497-507
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• 2017

Research on software education and linking and convergence of other subjects has been mainly focused on mathematics and science subjects. The dissatisfaction of various preferences and types of learning personality cause to learning gap. In addition, it is not desirable considering the solution of various fusion problems that can apply the computational thinking. In this way, it is possible to embrace the diverse tendencies and preferences of students through the linkage with the English subject, which is a linguistic approach that deviates from the existing mathematical and scientific approach. By combining similarities in the process of learning a new language of English education and software education. For this purpose, based on the analysis of teaching - learning model of elementary English subject and software education, we developed a class model by modifying existing English subject and software teaching - learning model to be suitable for linkage. Then, the learning elements applicable to software education were extracted from the contents of elementary school English curriculum, and a program applied to the developed classroom model was designed and the practical application method of learning was searched.

• Journal of The Korean Association of Information Education
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• v.23 no.4
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• pp.315-327
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• 2019

The purpose of this study was to propose an effective teaching and learning model by investigating the influencing factors of elementary school students' interest and computational thinking in software education based on flipped learning. To accomplish the purpose of the study, we developed a software education program based on flipped learning for elementary school students and applied qualitative research based on the interviews with the students and outputs from the learning process. The results of this study, First, factors affecting interest in the elementary school students' software education based on flipped learning were 'the expectation of class', 'authentic task', 'the accomplishment of task' and 'interaction with peers'. Second, the factor of enhancing computational thinking was 'the accomplishment of task', 'interaction with peers', and 'the teacher's meaningful feedback'.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.83-92
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• 2022

With the digital transformation of the entire industry, software competency has become the core competency for the future talent. However, it is difficult to find researches related to the corporate education for improving employee's software capability. Therefore, this study tried to verify the relationship between factors affecting the learning performance of employees in online software education. For this purpose, a survey of 223 employees with online software education experience was analyzed using the SPSS PROCESS macro. As a result of analysis, perceived usefulness and self-regulated learning have been found to have a significant multiple mediating effect on learning performance by digital literacy and satisfaction. This suggests that not only learner factors but also the characteristics of education should be considered. The results of this study are expected to be helpful in designing effective online education programs.

• Journal of Astronomy and Space Sciences
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• v.41 no.2
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• pp.61-78
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• 2024

This study developed a real-time spacecraft pose estimation algorithm that combined a deep learning model and the least-squares method. Pose estimation in space is crucial for automatic rendezvous docking and inter-spacecraft communication. Owing to the difficulty in training deep learning models in space, we showed that actual experimental results could be predicted through software simulations on the ground. We integrated deep learning with nonlinear least squares (NLS) to predict the pose from a single spacecraft image in real time. We constructed a virtual environment capable of mass-producing synthetic images to train a deep learning model. This study proposed a method for training a deep learning model using pure synthetic images. Further, a visual-based real-time estimation system suitable for use in a flight testbed was constructed. Consequently, it was verified that the hardware experimental results could be predicted from software simulations with the same environment and relative distance. This study showed that a deep learning model trained using only synthetic images can be sufficiently applied to real images. Thus, this study proposed a real-time pose estimation software for automatic docking and demonstrated that the method constructed with only synthetic data was applicable in space.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.319-326
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• 2013

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. 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. It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on infinite failure model and non-homogeneous Poisson Processes (NHPP). Statistical process control (SPC) can monitor the forecasting of software failure and thereby contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper, we proposed a control mechanism based on NHPP using mean value function of logarithmic hazard learning effects property.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.81-90
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• 2019

Since 2015, the government has been striving to strengthen the software capabilities required for future talent through software-oriented university in Korea. In the university selected as a software-oriented university, basic software education is given to all departments such as humanities, social science, engineering, natural science, arts and the sports within the university in order to foster convergent human resources with different knowledge and software literacy. In this paper, we analyze the contents of basic software education for twenty universities selected as software-oriented universities. As a result of analysis, most of the basic software education which is carried out to the students of the non-majors students was aimed at improvement of problem solving ability centered on computational thinking for future society and improvement of convergence ability based on computer science. It uses block-based educational programming language and text-based advanced programming language to adjust the difficulty of programming contents and contents reflecting characteristics of each major. Problem-based learning, project-based learning, and discussion method were used as the teaching and learning methods for problem solving. In the future, this paper will help to establish the systematic direction for basic software education of non-majors students.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.56-62
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• 2019

Recently, research using artificial neural networks has further expanded the field of neural network optimization and automatic structuring from improving inference accuracy. The performance of the machine learning algorithm depends on how the hyperparameters are configured. Open-source hyperparameter optimization software can be an important step forward in improving the performance of machine learning algorithms. In this paper, we review open-source hyperparameter optimization softwares.