• Journal of Korea Multimedia Society
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• v.1 no.2
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• pp.239-248
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• 1998

Software reengineering is making various research for solutions against problem of maintain existing system. Reengineering has a meaning of development of softwares on existing systems through the reverse-engineering and the forward-engineering. It extracts classes from existing system's softwares to increase the comprehension of the system and enhance the maintenability of softwares. Most of the important concepts used in reengineering is composition that is restructuring of the existing objects from other components. The classes and clusters in storage have structural relationship with system's main components to reuse in the higher level. These are referenced as dynamic informations through structuring an architect for each of them. The classes are created by extractor, searcher and composer through representing existing object-oriented source code. Each of classes and clusters extract refined informations through optimization. New architecture is created from the cluster based on its classes' relationship in storage. This information can be used as an executable code later on. In this paper, we propose the tools, it presented by this thesis presents a new information to users through analysing, based on reengineering, Object-Oriented informations and practicing composition methodology. These composite classes will increase reusability and produce higher comprehension information to consist maintainability for existing codes.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.6
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• pp.247-252
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• 2015

As software has gotten bigger in magnitude and the complexity of software has been increased, the maintenance has gained in-creasing attention for its significant impact on the cost. Identifiers have an impact on more than 90 percent of the readability which accounts for a majority portion of the maintenance activities. For this reason, the existing works focus on domain-specific features based on identifiers. However, their approaches have a limitation when either a class name does not reflect the intention of its context or a class naming is incorrect. Therefore, this paper suggests a series of class name validation process by extracting properties of classes, building learning model by applying a decision tree technique of machine learning, and generating a validation report containing the list of recommendable postfixes of classes to be validated. To evaluate this, four open source projects are selected and indicators such as precision, recall, and ROC curve present the value of this work when it comes to five specific postfixes including functional information on class names.

• The KIPS Transactions:PartA
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• v.12A no.3 s.93
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• pp.229-234
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• 2005

The virtual machine is a programming environment that supports device and platform independence. So far, virtual machines such as JVM and KVM have been used in a variety of environments for the Java language. Some virtual machines similar to them are also being developed and used. This paper Presents the experiences of extracting elements essential for small sized devices such as PDA from Java Class files(*.class) and designing a converted class file(*.rclass) for runtime efficiency by modifying its class file format and developing its translator. In addition, a virtual machine is developed to receive the translated class file entered and output the runtime results.

• Journal of radiological science and technology
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• v.30 no.4
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• pp.419-426
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• 2007

In this paper, the input pattern vectors are extracted and the learning algorithms is designed to recognize taste(bitter, sweet, sour and salty) pattern vectors. The signal intensity of taste are used to compose the input pattern vectors. The SOM(Self Organizing Maps) algorithm for taste pattern recognition is used to learn initial reference vectors and the ot-star learning algorithm is used to determine the class of the output neurons of the sunclass layer. The weights of the proposed algorithm which is between the input layer and the subclass layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVQ(Learning Vector Quantization) algorithm. The pattern vectors are classified into subclasses by neurons in the subclass layer, and the weights between subclass layer and output layer are learned to classify the classified subclass, which is enclosed a class. To classify the pattern vectors, the proposed algorithm is simulated with ones of the conventional LVQ, and it is confirmed that the proposed learning method is more successful classification than the conventional LVQ.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.4
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• pp.1942-1950
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• 2013

This paper suggests a new method of detecting attacks of network traffic by visualizing original traffic data and applying multi-class SVM (support vector machine). The proposed method first generates 2D images from IP and ports of transmitters and receivers, and extracts linear patterns and high intensity values from the images, representing traffic attacks. It then obtains variance of ports of transmitters and receivers and extracts the number of clusters and entropy features using ISODATA algorithm. Finally, it determines through multi-class SVM if the traffic data contain DDoS, DoS, Internet worm, or port scans. Experimental results show that the suggested multi-class SVM-based algorithm can more effectively detect network traffic attacks.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.95-103
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• 2006

In this paper, the input pattern vectors are extracted and the learning algorithms is designed to recognize EEG pattern vectors. The frequency and amplitude of alpha rhythms and beta rhythms are used to compose the input pattern vectors. And the algorithm for EEG pattern recognition is used SOM to learn initial reference vectors and out-star learning algorithm to determine the class of the output neurons of the subclass layer. The weights of the proposed algorithm which is between the input layer and the subclass layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVQ algorithm, and pattern vectors is classified into subclasses by neurons which is being in the subclass layer, and the weights between subclass layer and output layer is learned to classify the classified subclass, which is enclosed a class. To classify the pattern vectors of EEG, the proposed algorithm is simulated with ones of the conventional LVQ, and it was a confirmation that the proposed learning method is more successful classification than the conventional LVQ.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.1027-1030
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• 2000

객체 지향 프로그램의 이해 및 테스팅을 효과적으로 수행하기 위해서는 객체 간의 상호 작용을 우선 이해하여야 한다. UML로 작성된 시스템 명세에서는 각각의 클래스에 대한 행동이 UML 상태 다이어그램으로 기술되어 있어 전체 시스템의 행동을 유추하는데 어려움이 따른다. 이 연구에서는 객체 지향 프로그램의 상태 다이어그램을 기반으로 객체간 행동 테스팅을 수행하기 위해서 UML 상태 다이어그램들을 합성하여 객체간 행동을 추출, 생성하는 과정을 기술한다. 추출, 합성된 객체간 행동 모델은 기존의 널리 알려진FSM 기반 테스팅 기법들을 그대로 이용할 수 있다.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.4
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• pp.273-280
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• 2013

This paper presents novel OCS-LBP (Oriented Center Symmetric Local Binary Patterns) based on orientation of pixel gradient and image retrieval system based on BoF (Bag-of-Feature) and random forest classifier. Feature vectors extracted from training data are clustered into code book and each feature is transformed new BoF feature using code book. BoF features are applied to random forest for training and random forest having N classes is constructed by combining several decision trees. For testing, the same OCS-LBP feature is extracted from a query image and BoF is applied to trained random forest classifier. In contrast to conventional retrieval system, query image selects similar K-nearest neighbor (K-NN) classes after random forest is performed. Then, Top K similar images are retrieved from database images that are only labeled K-NN classes. Compared with other retrieval algorithms, the proposed method shows both fast processing time and improved retrieval performance.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06a
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• pp.116-118
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• 2012

안드로이드 앱 시장이 활성화되면서, 안드로이드 앱의 불법복제나 역공학 공격으로 인한 피해가 증가하고 있다. 앱 불법복제는 앱 판매 수익의 저하뿐만 아니라 개발자의 의지를 뺏고 개발 노력에 대해 상대적 박탈감을 주게 된다. 자바 프로그램의 경우 역공학으로 인해 바이트 코드에 존재하는 핵심 알고리즘이 쉽게 노출되어 지적재산권이 유출될 수 있다는 점에서 개발자나 개발사에게 심각한 위협이 되고 있다. 본 논문에서는 안드로이드 환경에서 앱에 대한 역공학 공격의 위협을 보이고, 역공학 방지 기법인 RACC를 제안한다. RACC는 보호할 핵심 클래스를 앱으로 부터 추출하여 바이트 코드 형태로 안전한 원격 서버에 관리하여 수행하며, 스마트폰(클라이언트)에는 저장하지 않는다. 스마트폰 앱이 해당 핵심 클래스를 호출하면, 그 호출이 원격 서버로 전송되어 수행된 후 결과가 스마트폰에 반환된다. 이처럼 핵심 클래스 코드가 클라이언트에 직접 노출 없이 원격지에서 관리되고 수행됨으로써 역공학 공격을 원천적으로 방지한다.

• The Transactions of the Korea Information Processing Society
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• v.4 no.1
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• pp.107-117
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• 1997

It is use in many researches that the s/w quality evaluation evaluates the developing system or the developed system, updates the problems and selects the reusable components from source code. In this paper, we propose the objective metric functions which can evaluate the reusability of candidates classes with the KHR system[11] and select a proper candidate. The quantitative quality we proposed have merits to compare and to evaluate the reusable candidates classes.