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Lecture Software engineering: Lecture 12 - TS. Đào Nam Anh

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Lecture "Software engineering - Lecture 12: Software metrics" has contents: Why measure software, fundamentals of measurement theory, use case points. Invite you to consult

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SOFTWARE ENGINEERING<br /> Lecture 12<br /> Software Metrics<br /> <br /> MBA Course Notes<br /> Dr. ANH DAO NAM<br /> 1<br /> <br /> Software Engineering<br /> Slides are from Ivan Marsic and Thomas E. Potok, and Richard A. Volz,<br /> modified by Anh Dao Nam<br /> Textbooks:<br /> Bruegge & Dutoit: Object-Oriented Software Engineering: Using UML,<br /> Patterns and Java, Third Edition, Prentice Hall, 2010.<br /> Miles & Hamilton: Learning UML 2.0, O’Reilly Media, 2006.<br /> Some interesting sources for the advanced material include:<br /> Richard A. Volz, Technical Metrics for Software<br /> R. Pressman, Software Engineering - A Practitioner's Approach, 6th ed.,<br /> 2005<br /> C. Ghezzi, M. Jazayeri, and D. Mandriolo, Fundamentals of Software<br /> Engineering. Prentice Hall, second ed., 2002<br /> A. Endres and D. Rombach, A Handbook of Software and Systems<br /> Engineering. The Fraunhofer IESE Series on Software Engineering,<br /> Pearson Education Ltd., 2003.<br /> S. Robertson and J. C. Robertson, Mastering the Requirements Process.<br /> Addison-Wesley Professional, second ed., 2006.<br /> <br /> 2<br /> <br /> Topics<br /> <br /> Why Measure Software<br /> Fundamentals of Measurement Theory<br /> Use Case Points<br /> <br /> 3<br /> <br /> Definitions<br /> Measure - quantitative indication of extent,<br /> amount, dimension, capacity, or size of some<br /> attribute of a product or process.<br /> E.g., Number of errors<br /> <br /> Metric - quantitative measure of degree to<br /> which a system, component or process<br /> possesses a given attribute. “A handle or<br /> guess about a given attribute.”<br /> E.g., Number of errors found per person hours<br /> expended<br /> <br /> 4<br /> <br /> Motivation for Metrics<br /> Estimate the cost & schedule of future projects<br /> Evaluate the productivity impacts of new tools and<br /> techniques<br /> Establish productivity trends over time<br /> Improve software quality<br /> Forecast future staffing needs<br /> Anticipate and reduce future maintenance needs<br /> <br /> 5<br /> <br />
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