Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Cloud Model based Efficiency Evaluation of Asset

클라우드 모델 기반의 자산 효율성 평가

  • Choi, Hanyong (Division of IT Convergence Engineering, Shinhan University)
  • 최한용 (신한대학교 IT융합공학부 컴퓨터공학전공)
  • Received : 2019.10.22
  • Accepted : 2019.12.20
  • Published : 2019.12.28
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Abstract

The software market has diversified service needs due to the expansion of the mobile market. To this end, the company intends to produce various apps by extending to the design domain based on the structured architectural assets of the domain market. In this study, we propose an evaluation model that can evaluate the efficiency for servicing assets that reflect the domain characteristics of architecture based on cloud. Based on the characteristics of ISO/IEC 25010 quality model of SQuaRE Series, a software evaluation standard, evaluation model sub-features for evaluating the efficiency of cloud-based asset data were constructed. When the architectural assets were designed as composite assets, they were designed to provide the flexibility of the evaluation model by applying the mandatory and optional evaluation elements of the sub-features that weighted the evaluation items according to the characteristics of the design domain.

소프트웨어 시장은 모바일 시장 확장으로 다품종의 다변화된 서비스 요구를 갖고 있다. 이를 위해 도메인 시장의 정형화된 아키텍처 자산을 기반으로 설계 도메인으로 확장하여 다양한 앱을 생산하고자 한다. 아키텍처 설계 정보는 클라우드를 기반으로 자산을 등록하고 관리할 수 있는 방법을 연구 중이다. 그리고 이와 같은 자산 관리 소프트웨어 시스템은 효율성을 평가하기 위한 방법이 필요하다. 본 연구에서는 선행된 자산관리 시스템을 클라우드 기반으로 서비스하기 위해 아키텍처 자산의 효율성을 평가하기 위한 평가모델을 제안하고자 한다. 아키텍처 자산의 효율성을 평가하기 위해 아키텍처 자산의 도메인 특성 값을 반영하기 위한 방법을 사용 하였다. 효율성 평가는 SQuaRE Series의 ISO/IEC 25010 표준을 기반으로 클라우드 기반의 자산데이터의 효율성을 평가하기 위한 평가모델 부 특성을 구성하였다. 아키텍처 자산은 복합자산으로 구성되어 설계되었을 때 설계 도메인의 특성에 따라 평가 항목의 가중치를 부여한 부 특성의 필수적 평가요소와 선택적 평가요소를 적용하도록 하여 평가모델의 설계 유연성을 제공하도록 하였다.

Keywords

References

  1. H. Y. Choi. (2018). Evaluation Method of Architecture Asset. Journal of Convergence for Information Technology, 8(5), 101-106. https://doi.org/10.22156/CS4SMB.2018.8.5.101
  2. Amazon Web Services. (2015). Amazon Web Services (AWS) CloudFormation. Retrieved from http://aws.amazon.com/cloudformation/.
  3. H. T. Mouftah & B. Kantarci. (2014). Communication Infrastructures for Cloud Computing, Hershey, PA, USA:UGI Global.
  4. M. Hajibaba & S. Gorgin. (2014). A review on modern distributed computing paradigms: Cloud computing jungle computing and fog computing, J. Comput. Inf. Tech, 22(2), 69-84 https://doi.org/10.2498/cit.1002381
  5. H. Y. Choi. (2016). MetaData Structure Design of Architecture Asset in DMI. Journal of Convergence for Information Technology, 6(4), 151-156.
  6. OMG. (2017). Reusable Asset Specification OMG Available Specification Version 2.2 . Name of Web Site. http://www.omg.org.
  7. ISO/IEC 25021. (2011). Software engineering - software product quality requirements and evaluation (SQuaRE) - quality measure elements.
  8. Fred B. Bryant & Albert Satorra. (2012). Principles and Practice of Scaled Difference Chi-Square Testing. Structural Equation Modeling: A Multidisciplinary Journal, 19(3), 372-398. https://doi.org/10.1080/10705511.2012.687671
  9. Koh, Seokha. (2016). Cause-and-Effect Perspective on Software Quality : Application to ISO/IEC 25000 Series SQuaRE's Product Quality Model, Journal of Information Technology Applications and Management, 23(3), 71-86. https://doi.org/10.21219/JITAM.2016.23.3.071
  10. Cai, X., Lyu, M.R., Wong, K. F. & Ko, R. (2000). Component-based software engineering: technologies, development frameworks, and quality assurance, schemes. Proc. Seventh Asia-Pacific Software Engineering Conf., APSEC 2000.
  11. Antonellis, P. et al. (2007). A data mining methodology for evaluating maintainability according to ISO/IEC-9126 software engineering - product quality standard. Special Session on System Quality and Maintainability - SQM2007.
  12. H. J. Jung, (2017). The Quantity Data Estimation for Software Quality Testing, Journal of the Korea Convergence Society, 8(10), 37-43. https://doi.org/10.15207/JKCS.2017.8.10.037
  13. Y. S. Jeong, Y. T. Kim & G. C. Park. (2017). A hierarchical property-based multi-level approach method for improves user access control in a cloud environment, Journal of the Korea Convergence Society , 8(11), 7-13. https://doi.org/10.15207/JKCS.2017.8.11.007
  14. B. K. Lee. (2014), A Study on Security of Virtualization in Cloud Computing Environment for Convergence Services, Journal of the Korea Convergence Society, 5(4), 93-99. https://doi.org/10.15207/JKCS.2014.5.4.093
  15. Krahn, H., Rumpe, B., Volkel & S. MontiCore. (2010). a framework for compositional development of domain specific languages. STTT, 12(5), 353-372. https://doi.org/10.1007/s10009-010-0142-1
  16. Simona Bernardi, José Merseguer & Dorina C. Petriu. (2012). Dependability modeling and analysis of software systems specified with UML. ACM Computing Surveys. 45(1) .
  17. H. Y. Choi & S. H. Sim. (2015). A Study on Software Development method based on DMI. ICSMB, 2(1), 359-360.
  18. H. Y. Choi & S. H. Sim. (2019). RAMS architecture based on cloud service, International Journal of Innovative Technology and Exploring Engineering, 8(4S2), 431-434.