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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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Presenter

Measuring the Structural Quality of Business Applications2011Agile Conference111Bill Curtis Jay SappidiJitendraSubramanyam

presenter :林賢勁


Outline

Outline

  • Introduction

  • The sample and data

  • Security

  • Changeability

  • Performance

  • Effect of size on quality

  • Summary

  • Reference


Introduction

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


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.


Security

Security


Changeability

Changeability


Performance

Performance


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.


Summary

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.


References

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.


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