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Measuring the Structural Quality of Business Applications 2011 Agile Conference 1 1 1 Bill Curtis Jay Sappidi Jitendra Subramanyam. presenter :林賢勁. Outline. Introduction The sample and data Security Changeability Performance Effect of size on quality Summary Reference.

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Measuring the Structural Quality of Business Applications2011Agile Conference111Bill Curtis Jay SappidiJitendraSubramanyam

presenter :林賢勁

  • Introduction
  • The sample and data
  • Security
  • Changeability
  • Performance
  • Effect of size on quality
  • Summary
  • Reference
  • Provide an objective, empirical foundation for evaluating
  • To help IT organizations make visible the costs and risks
  • Identify the factors that most affect quality
  • Internal quality characteristics are critical because they are often difficult to detect through standard testing
the sample and data
The Sample And Data
  • The applications range from 10,000 to 5 million lines of code (LOC), with a mean of 374,220 LOC.
  • The languages : COBOL, Java EE, .NET, C, C++, and ABAP.
effect of size on quality
Effect Of Size On Quality
  • The relationship between size and quality : various quality characteristics into a combined Total Quality Index with application size.
  • COBOL does not encourage modularity, resulting in applications possessing many large and complex components.
  • Automated structural quality detection techniques are important to the Agile Methods community in several ways.
  • automated analysis and measurement of structural quality should be incorporated into an automated continuous build environment.
  • The severity should be prioritized for immediate remediation.
  • To provide a strong measure of technical debt, a critical management parameter for evaluating cost of application ownership and risk to the business.
  • [1] Spinellis, D. (2006). Code Quality: The Open Source Perspective. Boston: Addison-Wesley.
  • [2] Curtis, B., Sheppard, S.B., and Milliman, P. (1979b). Third time charm: Stronger prediction of programmer performance by software complexity metrics. Proceedings of the 4th International Conference on Software Engineering.Washington, DC: IEEE Computer Society, 356-360.
  • [3] Boehm, B.W., Brown, J.R., & Lipow, M. (1976). Quantitative evaluation ofsoftware quality. Proceedings of the 2nd International Conference on Software Engineering. Los Alamitos, CA: IEEE Computer Society Press, 592-605.
  • [4] Sappidi, J., Curtis, B., & Subramanyam, J. (2010). CAST Worldwide Application Software Quality Study—2010. New York: CAST Software.
Q & A
  • Thanks for your listening