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About this product
- Author(s)James Bieman,Norman Fenton
- PublisherTaylor & Francis Inc
- Date of Publication01/10/2014
- GenreComputing: Professional & Programming
- Series TitleChapman & Hall/CRC Innovations in Software Engineering and Software Development Series
- Place of PublicationBosa Roca
- Country of PublicationUnited States
- ImprintCRC Press Inc
- Content Note155 black & white illustrations, 52 black & white tables
- Weight1020 g
- Width156 mm
- Height235 mm
- Format DetailsUnsewn / adhesive bound
- Edition Statement3rd Revised edition
- Table Of ContentsFundamentals of Measurement and Experimentation Measurement: 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 Measurement Measuring 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 Environment Wider Aspects of Software Reliability Appendix: Solutions to Selected Exercises Bibliography Index Summary, Exercises, and Further Reading appear at the end of each chapter.
- Author BiographyNorman 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.
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