• Journal of Software Engineering Society
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• v.26 no.1
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• pp.17-29
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• 2013

Software System Evolution is more planned and systematic maintenance process as well as a type of maintenance. The research of software evolution provides basis of decisions for maintenance value through cost-benefit analysis of legacy system and architecture-based software evolution enables engineers to reduce maintenance cost by automation of repetitive evolution tasks. This paper proposes a framework for software system evolution using architectural transformation pattern.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.755-766
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• 2009

Software architecture allows us to make many decisions about a software system and analyze it even before it exists in order to make systematic and planned development possible. Similarly, architecture-based software evolution planningmakes planned evolution possible by allowing us to make many decisions about evolution of a software system and analyze its evolution at the level of architecture design before software evolution is realized. In this paper, we develop a framework for architecture-based software evolution planning for the class of deterministic evolution plans by defining and relating various essential concepts and developing its valuationmechanism so that a plan with the greatest value among candidate plans can be selected as an optimal plan. A case study is conducted for explicating the framework and exemplifying its usage.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.413-416
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• 2008

본 연구에서는 일반적인 시스템 통합 프로젝트에서 발생하는 문제점들을 살펴보면서 새로운 UI 기반의 시스템 통합에 대한 방법을 제시하고자 한다. 시스템통합 프로젝트 진행 시 많은 비용과 시간이 소요되고 있으며 업무시스템의 통합 및 인터페이스 산출물에 대한 검증으로 다양한 이슈들이 발생하고있다. 특히 통합 요구사항의 출현 및 EAI(Enterprise Application Integration), B2Bi(Business-to-Business Integration)에 대한 다양한 요구사항으로 일반적인 어플리케이션 통합프로젝트의 구성에 대한 논의와 새로운 시스템통합 방법론에 대한 정의가 필요하다. 이러한 문제를 해결하기 위하여 기존 방식에서 좀더 진화된 비주얼한 웹 화면통합 기반의 SOA(Service Oriented Architecture) 구축결과 물을 기반으로, VISUAL SOA 기반 프레임워크를 제안하고자 한다.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.293-298
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• 2014

Detecting and understanding the changes between RDF data is crucial in the evolutionary process, synchronization system, and versioning system on the web of data. However, current researches on detecting changes still remain unsatisfactory in that they did neither consider the large scale of RDF data nor accurately produce the RDF deltas. In this paper, we propose a scalable and effective change detection using a MapReduce framework which has been used in many fields to process and analyze large volumes of data. In particular, we focus on the structure-based change detection that adopts a strategy for the comparison of blank nodes in RDF data. To achieve this, we employ a method which is composed of two MapReduce jobs. First job partitions the triples with blank nodes by grouping each triple with the same blank node ID and then computes the incoming path to the blank node. Second job partitions the triples with the same path and matchs blank nodes with the Hungarian method. In experiments, we show that our approach is more accurate and effective than the previous approach.