Software Metrics A Rigorous and Practical Approach, Third Edition

A Framework for Managing, Measuring, and Predicting Attributes of Software Development Products and ProcessesReflecting the immense progress in the development and use of software metrics in the past decades, Software Metrics: A Rigorous and Practical Approach, Third Edition provides an up-to-date, accessible, and comprehensive introduction to software metrics. Like its popular predecessors, this third edition discusses important issues, explains essential concepts, and offers new approaches for tackling long-standing problems. New to the Third EditionThis edition contains new material relevant to object-oriented design, design patterns, model-driven development, and agile development processes. It includes a new chapter on causal models and Bayesian networks and their application to software engineering. This edition also incorporates recent references to the latest software metrics activities, including research results, industrial case studies, and standards. Suitable for a Range of ReadersWith numerous examples and exercises, this book continues to serve a wide audience. It can be used as a textbook for a software metrics and quality assurance course or as a useful supplement in any software engineering course. Practitioners will appreciate the important results that have previously only appeared in research-oriented publications. Researchers will welcome the material on new results as well as the extensive bibliography of measurement-related information. The book also gives software managers and developers practical guidelines for selecting metrics and planning their use in a measurement program.

Fundamentals of Measurement and ExperimentationMeasurement: What Is It and Why Do It? Measurement in Everyday Life Measurement in Software Engineering Scope of Software Metrics The Basics of Measurement The Representational Theory of Measurement Measurement and Models Measurement Scales and Scale Types Meaningfulness in Measurement A Goal-Based Framework for Software Measurement Classifying Software Measures Determining What to Measure Applying the Framework Software Measurement Validation Performing Software Measurement Validation Empirical Investigation Principles of Empirical Studies Planning Experiments Planning Case Studies as Quasi-Experiments Relevant and Meaningful Studies Software Metrics Data Collection Defining Good Data Data Collection for Incident Reports How to Collect Data Reliability of Data Collection Procedures Analyzing Software Measurement Data Statistical Distributions and Hypothesis Testing Classical Data Analysis Techniques Examples of Simple Analysis Techniques More Advanced Methods Multicriteria Decision Aids Overview of Statistical Tests Metrics for Decision Support: The Need for Causal Models From Correlation and Regression to Causal Models Bayes Theorem and Bayesian Networks Applying Bayesian Networks to the Problem of Software Defects Prediction Bayesian Networks for Software Project Risk Assessment and Prediction Software Engineering MeasurementMeasuring Internal Product Attributes: Size Properties of Software Size Code Size Design Size Requirements Analysis and Specification Size Functional Size Measures and Estimators Applications of Size Measures Problem, Solution Size, Computational Complexity Measuring Internal Product Attributes: Structure Aspects of Structural Measures Control Flow Structure of Program Units Design-Level Attributes Object-Oriented Structural Attributes and Measures No Single Overall "Software Complexity" Measure Measuring External Product Attributes Modeling Software Quality Measuring Aspects of Quality Usability Measures Maintainability Measures Security Measures Software Reliability: Measurement and Prediction Basics of Reliability Theory The Software Reliability Problem Parametric Reliability Growth Models Predictive Accuracy Recalibration of Software Reliability Growth Predictions Importance of the Operational EnvironmentWider Aspects of Software Reliability Appendix: Solutions to Selected ExercisesBibliographyIndex Summary, Exercises, and Further Reading appear at the end of each chapter.

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.