• Journal of Information Technology Applications and Management
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• v.27 no.6
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• pp.101-116
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• 2020

This study attempts to derive critical success factors and prioritize activities for implementing the automation of software test. Software testing proceeds in five steps of test plan, test case design, test scripting & test environment setup, test execution & evaluation, and test closure. From the literature, twenty influence factors in the five steps were identified. Focus Group Interviews with ten experts in software testing selected fourteen factors on the basis of importance and feasibility. Some factors were further divided to clarify their meanings and the number of influence factors finally became twenty one. A survey on the influence factors was conducted with 75 IT professionals. An importance-feasibility analysis was applied to the survey responses and classified the influence factors into four groups of essential factors, critical factors, low priority factors, and minimal factors. Essential factors include objectives for test automation, strategies for test automation, test automation training, test automation education, setting up test automation environment, setting up test environment, test automation tool selection. Critical factors include management's continued support, management's continued interest, role of automation engineers, and skills of automation engineers. This result suggests that a lot of efforts should be put into SW test plan for the successful SW test automation and SW automation should proceed in consideration with the priorities and resource consumption.

• Journal of the Korea Society of Computer and Information
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• v.20 no.10
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• pp.45-51
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• 2015

In this paper, we propose a way to reduce the SW reliability test time and human errors by utilizing the Lean Six Sigma methodology as a troubleshooting tool. Recently, SW technology is rapidly changing and the importance of SW management has been recognized. Accordingly, the SW reliable test for the SW development outcomes in one of the methods for SW quality improvement is required. However, it consumes a lot of development costs, has the time constraints and human errors in SW reliable test, so, it is difficult to meet the requirements for SW test.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.10
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• pp.892-897
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• 2017

As the weapon system SW development and management manual of the DAPA, which is the regulation for the overall weapon system SW development, is revised, the level and scope of SW reliability test are upgraded to improve the reliability and quality of SW. It is a big burden for SW developers. In particular, the dynamic test requires a schedule and manpower required to implement the weapon system SW, and should be performed every time the source code changes, not just one time. In this paper, we propose a regression test automation program(VectorCast Environment Manager) that performs a dynamic test using VectorCast, a dynamic test tool, and then performs a regression test automatically by minimizing human intervention in the regression test of dynamic test due to the change of the source code.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.6
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• pp.329-338
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• 2019

Today's embedded software (SW) developments are mostly preceded by composing Software Requirement Specification (SRS). In particular, in the domain of weapon systems, it is essential to have a systematic method for the verification of the SW functionality. To be more specific, it is crucial to check if the SW functionality is implemented as described in SRS, so-called SW suitability verification. Unfortunately, existing static or dynamic SW testing methods are not sufficient to evaluate suitability with SRS since those testings only verify the robustness of the SW codes. In this paper, we propose an automatic embedded SW suitability verification framework which is based on a structured SRS. The major challenge in the automation of this verification framework is how to get rid of ambiguities in SRS. In order to overcome this challenge, we propose a structured SRS description framework and the supporting toolchain for that. We show how the proposed framework is applied to an actual SRS of a weapon system.

• Journal of Creative Information Culture
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• v.7 no.4
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• pp.207-215
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• 2021

As computational thinking has been noted as an important competency worldwide, SW education was introduced in the 2015 revised curriculum, and SW education has been applied to the curriculum from 2018. However, in a poor educational environment, the educationally underprivileged class is in the blind spot of SW education and is not receiving systematic SW education. Therefore, this study analyzed the effect of conducting a non-face-to-face SW online education program for 267 underprivileged elementary school students in education at a time when non-face-to-face online education was being conducted through the COVID-19 mass infectious disease. As a result of conducting the computational thinking ability test, which abstraction, problem decomposition, algorithm, automation, and data processing, before and after education, the overall score of computational thinking and the score of all five factors were statistically significantly increased(p<0.001). Among the five factors, there was the highest score improvement in data processing score. These results suggest that the non-face-to-face SW online education program is effective in improving the computational thinking ability of elementary school students from the educational underprivileged class.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.77-84
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• 2021

The purpose of this study is to propose AI maker coding education as a way to improve computational thinking(CT), which is an essential competence for problem-solving capability in modern society, and to analyze the effectiveness of this education on improving CT in elementary school students. For the research, 5 students from 4th graders and 5 students from 6th graders were recruited, and AI maker coding education was planned in 8 sessions to form classes from basic block coding and maker education to real-life problem solving. To analyze the effectiveness of AI maker coding education, pre- and post-CT examinations were performed. The test results confirmed that AI maker coding education had a significant effect on "abstraction", "algorithm", and "data processing" in the five CT components, and confirmed that there was no correlation in "problem resolution" and "automation". Overall, the average score of all students increased, and the deviation between students decreased, confirming that AI maker coding education was effective in improving CT.