• Journal of Software Engineering Society
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• v.29 no.2
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• pp.13-20
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• 2020

The exploratory test was introduced as a desirable test method due to its fast development cycle, but it is not actively adopted because documentation and analysis of the test range are required for application. On the other hand, keyword-based testing has been introduced as a way to save resources and facilitate maintenance, but it is difficult to plan tests in advance due to the large number of variables such as data, settings, interactions, sequence and timing. However, in keyword-based testing, you can create a test case based on keywords by presenting clear criteria and methods for creating keywords and applying the exploration testing process. In this paper, we propose a model that automates exploratory tests based on keywords. To verify the effectiveness, we compared the general keyword-based test(KBT) and keyword-based exploratory test(KBET), and compared with the exploratory normal test case(ETC) and keyword-based exploratory test(KBET).

• The KIPS Transactions:PartD
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• v.13D no.2 s.105
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• pp.215-222
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• 2006

The importance of mobile software test is being addressed to improve the productivity and reliability of the software. Test automation technique based on mobile platform is required for effective application of mobile software test. That is, a technique is needed to generate test case for mobile platform API. When test case generated, software productivity and reliability are improved, while test duration and cost are decreased. In this paper, we identified test case generation scope through previous works about test automation, suggested keyword driven method, a test case generation technique on J2ME platform, and recognized that proposed method can be applicable to generating test case based on J2ME platform.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.331-334
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• 2019

일반적인 키워드 기반 테스트는 기능 위주의 키워드를 작성하여 테스트를 자동화하여 비용은 적게 들지만 활용도가 높은 테스트를 자동화기 어렵다. 한편 탐색적 테스트는 리스크 기반으로 차터를 작성하여 짧은 시간동안 많은 에러를 탐지하는 장점이 있으나, 문서화가 미흡하다는 단점이 있다. 위와 같은 단점을 보완하기 위하여 탐색적 테스트의 기본 원리를 고수하면서 효율적 키워드 기반 자동화가 가능한 RAKTA(Record And Keyword-based Test Automation) 방법론을 제안한다. RAKTA는 오픈 소스 키워드 기반 자동화 프레임워크인 로봇 프레임워크의 기술을 사용하여, 키워드 기반과, 탐색적 테스트의 장점을 뽑아 효율적으로 테스트 자동화하여 비용을 줄이고 많은 에러를 탐지할 수 있다. 또한 본 논문에서는 RAKTA 방법론을 활용한 여러 가지 키워드 재사용 사례와 기존 조직에서 사용하던 테스트 스크립트를 혼합하여 통합 테스트, 인수 테스트, 설치 테스트를 자동화하는 방법을 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.1996-2011
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• 2021

To understand a trend is to explore the intricate process of how something or a particular situation is constantly changing or developing in a certain direction. This exploration is about observing and describing an unknown field of knowledge, not testing theories or models with a preconceived hypothesis. The purpose is to gain knowledge we did not expect and to recognize the associations among the elements that were suspected or not. This generally requires examining a massive amount of data to find information that could be transformed into meaningful knowledge. That is, looking through the lens of big-data analytics with an inductive reasoning approach will help expand our understanding of the complex nature of a trend. The current study explored the trend of well-being in South Korea using big-data analytic techniques to discover hidden search patterns, associative rules, and keyword signals. Thereafter, a theory was developed based on inductive reasoning - namely the hook, upward push, and downward pull to elucidate a holistic picture of how big-data implications alongside social phenomena may have influenced the well-being trend.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.2
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• pp.111-120
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• 2015

Protein named entity identification is one of the most essential and fundamental predecessor for extracting information about protein-protein interactions from biomedical literature. In this paper, we explore the use of abstracts of biomedical literature in MEDLINE for protein name identification and present the results of the conducted experiments. We present a robust and effective approach to classify biomedical named entities into protein and non-protein classes, based on a rich set of features: orthographic, keyword, morphological and newly introduced Protein-Score features. Our procedure shows significant performance in the experiments on GENIA corpus using Random Forest, achieving the highest values of precision 92.7%, recall 91.7%, and F-measure 92.2% for protein identification, while reducing the training and testing time significantly.

• Korean Journal of Construction Engineering and Management
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• v.9 no.4
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• pp.193-205
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• 2008

Quality is an important keyword representing the corporate competitiveness and image in today' s construction industry. Especially in concrete construction, any problems or defects in fresh concrete can significantly degrade the entire quality and performance of the facility built. Thus, adequate quality inspection and testing must be exercised over the fresh concrete, if concrete with the required strength, durability and appearance is to be obtained. The testing of concrete delivered to the construction job site involves testing of fresh concrete and performing strength tests on hardened concrete. The principal tests conducted on fresh concrete include the slump test and tests for air and salt content. The temperature of fresh concrete should be checked out hot or cold weather concreting. The 7-day and 28-day strength of hardened concrete are also determined by compression tests on usually cylinder samples. However, it is very complex and time-consuming process requiring a lot of efforts to document those on-site concrete testing results and to accumulate their historical data. The primary objective of this study is to suggest a unique PDA and web-based system which enables an on-site quality manager to effectively conduct the concrete inspection and testing, automatically document and accumulate the collected historical data, and promptly obtain the approval from supervisors. Finally, it is anticipated that the effective use of the proposed PDA and web-based system would be able to improve reliability of the concrete quality inspection and testing data as well as significantly reduce the approval process.

• Smart Structures and Systems
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• v.30 no.2
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• pp.145-157
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• 2022

The design of safe and economical structures depends on the reliable live load from load survey. Live load surveys are traditionally conducted by randomly selecting rooms and weighing each item on-site, a method that has problems of low efficiency, high cost, and long cycle time. This paper proposes a deep learning-based method combined with Internet big data to perform live load surveys. The proposed survey method utilizes multi-source heterogeneous data, such as images, voice, and product identification, to obtain the live load without weighing each item through object detection, web crawler, and speech recognition. The indoor objects and face detection models are first developed based on fine-tuning the YOLOv3 algorithm to detect target objects and obtain the number of people in a room, respectively. Each detection model is evaluated using the independent testing set. Then web crawler frameworks with keyword and image retrieval are established to extract the weight information of detected objects from Internet big data. The live load in a room is derived by combining the weight and number of items and people. To verify the feasibility of the proposed survey method, a live load survey is carried out for a meeting room. The results show that, compared with the traditional method of sampling and weighing, the proposed method could perform efficient and convenient live load surveys and represents a new load research paradigm.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.294-300
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• 2021

The current society relies upon social media on an everyday basis, which contributes to finding which of the following supervised machine learning algorithms used in sentiment analysis have higher accuracy in detecting Malay internet slang and short forms which can be offensive to a person. This paper is to determine which of the algorithms chosen in supervised machine learning with higher accuracy in detecting internet slang and short forms. To analyze the results of the supervised machine learning classifiers, we have chosen two types of datasets, one is political topic-based, and another same set but is mixed with 50 tweets per targeted keyword. The datasets are then manually labelled positive and negative, before separating the 275 tweets into training and testing sets. Naïve Bayes and Random Forest classifiers are then analyzed and evaluated from their performances. Our experiment results show that Random Forest is a better classifier compared to Naïve Bayes.

• Industry Promotion Research
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• v.8 no.4
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• pp.251-256
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• 2023

The purpose of this study is to find out how much research has been done on dream analysis based on S.Freud's psychoanalytic theory, and to suggest the necessity of dream research and follow-up research on dream research. The research method was based on analysis of domestic academic journals from 2019 to 2023, a study on S.Freud's dream analysis. Among them, the data were collected and organized through a keyword classification process from the Research Information Service (RISS) and the Korean Journal Citation Index (KCI). The classification categories were psychoanalysis, domestic academic journals, dream analysis, dream interpretation, dream analysis research trends, and dream research trends. In particular, psychoanalysis, dream analysis, domestic academic journals, and research trends were searched. The conclusion was drawn as follows. First, studies on research trends on dream analysis in domestic academic journals did not occupy a large proportion. Second, the ratio of research trends centered on dream analysis keywords was also significantly low. Third, the use and frequency of dream analysis was low. Fourth, research on Korean testing tools based on dream analysis is needed.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.109-131
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• 2014

As the demand of nuclear power plant equipment is continuously growing worldwide, the importance of handling nuclear strategic materials is also increasing. While the number of cases submitted for the exports of nuclear-power commodity and technology is dramatically increasing, preadjudication (or prescreening to be simple) of strategic materials has been done so far by experts of a long-time experience and extensive field knowledge. However, there is severe shortage of experts in this domain, not to mention that it takes a long time to develop an expert. Because human experts must manually evaluate all the documents submitted for export permission, the current practice of nuclear material export is neither time-efficient nor cost-effective. Toward alleviating the problem of relying on costly human experts only, our research proposes a new system designed to help field experts make their decisions more effectively and efficiently. The proposed system is built upon case-based reasoning, which in essence extracts key features from the existing cases, compares the features with the features of a new case, and derives a solution for the new case by referencing similar cases and their solutions. Our research proposes a framework of case-based reasoning system, designs a case-based reasoning system for the control of nuclear material exports, and evaluates the performance of alternative keyword extraction methods (full automatic, full manual, and semi-automatic). A keyword extraction method is an essential component of the case-based reasoning system as it is used to extract key features of the cases. The full automatic method was conducted using TF-IDF, which is a widely used de facto standard method for representative keyword extraction in text mining. TF (Term Frequency) is based on the frequency count of the term within a document, showing how important the term is within a document while IDF (Inverted Document Frequency) is based on the infrequency of the term within a document set, showing how uniquely the term represents the document. The results show that the semi-automatic approach, which is based on the collaboration of machine and human, is the most effective solution regardless of whether the human is a field expert or a student who majors in nuclear engineering. Moreover, we propose a new approach of computing nuclear document similarity along with a new framework of document analysis. The proposed algorithm of nuclear document similarity considers both document-to-document similarity (${\alpha}$) and document-to-nuclear system similarity (${\beta}$), in order to derive the final score (${\gamma}$) for the decision of whether the presented case is of strategic material or not. The final score (${\gamma}$) represents a document similarity between the past cases and the new case. The score is induced by not only exploiting conventional TF-IDF, but utilizing a nuclear system similarity score, which takes the context of nuclear system domain into account. Finally, the system retrieves top-3 documents stored in the case base that are considered as the most similar cases with regard to the new case, and provides them with the degree of credibility. With this final score and the credibility score, it becomes easier for a user to see which documents in the case base are more worthy of looking up so that the user can make a proper decision with relatively lower cost. The evaluation of the system has been conducted by developing a prototype and testing with field data. The system workflows and outcomes have been verified by the field experts. This research is expected to contribute the growth of knowledge service industry by proposing a new system that can effectively reduce the burden of relying on costly human experts for the export control of nuclear materials and that can be considered as a meaningful example of knowledge service application.