Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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A Study on the Complexity Measurement of Architecture Assets

아키텍처 자산의 복잡도 측정에 관한 연구

  • Choi, Han-Yong (Division of IT Convergence Engineering, Shinhan University)
  • 최한용 (신한대학교 IT융합공학부)
  • Received : 2017.09.29
  • Accepted : 2017.10.20
  • Published : 2017.10.31
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Abstract

In this paper, we propose a method to measure the complexity of assets when a software component is constructed as a basic asset, a standardized design model is acquired, and a reusable extended asset is designed based on the standardized design model. However, each asset of our proposed asset management system consists of composite assets that combine assets of two domains. So this method can not make accurate measurements. Therefore, the complexity of the overall asset can be measured by reflecting the property value of the basic asset stored under the architecture. In conclusion, it is possible to measure the composite-complexity of a composed asset that is inversely proportional to cohesion and proportional to the cumulative sum of the associated values of each asset in the asset-related design.

자산의 복잡성을 측정하기 위해 프로그램의 논리적인 복잡도의 측정을 제공하는 척도를 베이스로 하여 각 자산의 특징 값을 표현하고 있는지 평가하는 방법을 사용 한다. 본 연구에서는 소프트웨어 컴포넌트를 기본자산으로 구성하여 표준화된 설계모형을 확보하고, 이를 기반으로 재사용 가능한 확장된 자산을 설계할 경우 자산의 복잡도를 측정하기 위한 방안을 제시하고자 한다. 그러나 우리가 제안하는 자산관리 시스템의 각 자산은 두 가지 영역의 자산을 합성한 복합자산으로 구성되어 있으므로 이 방법만으로는 정확한 측정을 하기 어렵다. 따라서 아키텍처 하부에 저장된 기본 자산의 특성 값을 반영하여야 전체적인 자산의 복잡성을 측정가능하다. 따라서 응집력에 반비례하고 자산연관도안의 각 자산에 대한 연관값의 누적합에 비례하는 합성자산의 복잡성을 측정 가능하다.

Keywords

References

  1. R. S. Pressman. (2005). Software engineering: a practitioner's approach. London : Palgrave Macmillan.
  2. H. Y. Choi. (2016). MetaData Structure Design of Architecture Asset in DMI. Journal of IT Convergence Society for SMB, 6(4), 151-156. DOI : 10.22156/cs4smb.2016.6.4.151
  3. OMG. (2017). Reusable Asset Specification OMG Available Specification Version 2.2. OMG. http://www.omg.org.
  4. K. Pohl, G. Bockle & F. J. van Der Linden. (2005). Software product line engineering: foundations, principles and techniques. Berlin : Springer Science & Business Media.
  5. S. Y. Min, S. H. Park & N. H. Lee. (2011). SW Quality of Convergence Product: Char acter istics, Improvement Str ategies and Alternatives. Journal of IT Convergence Society for SMB, 1(1), 19-28.
  6. N. I. Altintas, S. Cetin, A. H. Dogru & H. Oguztuzun. (2012). Modeling product line software assets using domain-specific kits. IEEE transactions on software engineering, 38(6), 1376-1402. DOI : 10.1109/tse.2011.109
  7. H. Krahn, B. Rumpe & S. Volkel. (2010). MontiCore: a framework for compositional development of domain specific languages. International Journal on Software Tools for Technology Transfer (STTT), 12(5), 353-372. DOI : 10.1007/s10009-010-0142-1
  8. S. Moser & V. B. Misic. (1997). Measuring class coupling and cohesion: a formal metamodel approach. In Software Engineering Conference (pp. 31-40). USA : IEEE. DOI : 10.1109/apsec.1997.640159
  9. S. Bernardi, J. Merseguer & D. C. Petriu. (2012). Dependability modeling and analysis of software systems specified with UML. ACM Computing Surveys, 45(1), 2. DOI : 10.1145/2379776.2379778
  10. A. B. Younes, Y. B. Hlaoui & L. J. B. Ayed. (2014). A meta-model transformation from uml activity diagrams to event-b models. 2014 IEEE 38th International (pp. 740-745). IEEE. USA : IEEE. DOI : 10.1109/compsacw.2014.119
  11. H. Choi & S. Sim. (2015). A Study on Software Development method based on DMI. International Conference for Small & Medium Business (pp. 359-360). Seoul : ICSMB.
  12. F. B. Bryant & A. Satorra. (2012). Principles and practice of scaled difference chi-square testing. Structural Equation Modeling: A Multidisciplinary Journal, 19(3), 372-398. DOI : 10.1080/10705511.2012.687671
  13. H. Choi & S. Sim. (2017). A Study on the Optimization of Architecture Assets in DMI. Journal of Advanced Research in Dynamical and Control Systems, 143-149.