Table of Contents

Fundamentals of Measurement and Experimentation: Measurement: What Is It and Why Do It? The Basics of Measurement. A Goal-Based Framework for Software Measurement. Empirical Investigation. Software Metrics Data Collection. Analyzing Software Measurement Data. Metrics for Decision Support: The Need for Causal Models. Software Engineering Measurement: Measuring Internal Product Attributes: Size. Measuring Internal Product Attributes: Structure. Measuring External Product Attributes. Software Reliability: Measurement and Prediction. Appendix. Bibliography. Index.

About the Author

Norman Fenton, PhD, is a professor of risk information management at Queen Mary London University and the chief executive officer of Agena, a company that specializes in risk management for critical systems. He is renowned for his work in software engineering and software metrics. His current projects focus on using Bayesian methods of analysis to risk assessment. He has published 6 books and more than 140 refereed articles and has provided consulting to many major companies worldwide.

James M. Bieman, PhD, is a professor of computer science at Colorado State University, where he was the founding director of the Software Assurance Laboratory. His research focuses on the evaluation of software designs and processes, including ways to test nontestable software, techniques that support automated software repair, and the relationships between internal design attributes and external quality attributes. He serves on the editorial boards of the Software Quality Journal and the Journal of Software and Systems Modeling.

Reviews

"The wait for a new edition of this book is over. Long considered the go-to text for its thorough coverage of software measurement and experimentation, the new edition succeeds splendidly in bringing the field up to date while including new and important topics. … updated with the latest results from recent advances in software measurement research and practice. … The authors do an outstanding job of balancing formal analysis topics with examples that ground the reader in practical application. … Both researchers and practitioners alike will gain a valuable understanding of why measurement is critical for quality improvements in software development processes and software products. … With this updated edition, this book solidifies its standing as the most complete reference text for software measurement."
—Computing Review, April 2015"I have been using this book as my primary reference on software metrics for over 20 years now. It still remains the best book by far on the science and practice of software metrics. This latest edition has some important updates, especially with the inclusion of material on Bayesian networks for prediction and risk assessment."
—Paul Krause, University of Surrey, Guildford, UK"Great introduction to software metrics, measurement, and experimentation. This will be a must-read for my software engineering students."
—Lukasz Radlinski, PhD, West Pomeranian University of Technology, Szczecin, Poland"I have loved this book from the first edition and with each new edition it just keeps getting better and better. I use this book constantly in my software engineering research and always recommend it to students. It is so much more than a software metrics book; to me it is an essential companion to rigorous empirical software engineering."
—Dr. Tracy Hall, Department of Computer Science, Brunel University, Uxbridge, UK"This new edition of Software Metrics succeeds admir