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presenter ：林賢勁

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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presenter ：林賢勁

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  1. Measuring the Structural Quality of Business Applications2011Agile Conference111Bill Curtis Jay SappidiJitendraSubramanyam presenter ：林賢勁

  2. Outline • Introduction • The sample and data • Security • Changeability • Performance • Effect of size on quality • Summary • Reference

  3. Introduction • 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

  4. 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.

  5. Security

  6. Changeability

  7. Performance

  8. 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.

  9. Summary • 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.

  10. References • [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.

  11. Q & A • Thanks for your listening

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