The role of metrics and models in software development; Software metrics; Measurement and analysis; Small scale experiments, micro-models of effort, and programming techniques; Macro-models of productivity; Macro-models for effort estimation; Defect models; The future of software engineering metrics and models; References; Appendices; Index.
""This is the single best book on software quality engineering and metrics that I've encountered."" --Capers Jones, from the Foreword"Metrics and Models in Software Quality Engineering, Second Edition," is the definitive book on this essential topic of software development. Comprehensive in scope with extensive industry examples, it shows how to measure software quality and use measurements to improve the software development process. Four major categories of quality metrics and models are addressed: quality management, software reliability and projection, complexity, and customer view. In addition, the book discusses the fundamentals of measurement theory, specific quality metrics and tools, and methods for applying metrics to the software development process.New chapters bring coverage of critical topics, including: In-process metrics for software testingMetrics for object-oriented software developmentAvailability metricsMethods for conducting in-process quality assessments and software project assessmentsDos and Don'ts of Software Process Improvement, by Patrick O'TooleUsing Function Point Metrics to Measure Software Process Improvement, by Capers Jones In addition to the excellent balance of theory, techniques, and examples, this book is highly instructive and practical, covering one of the most important topics in software development--quality engineering. 0201729156B08282002
Summary Software Development Metrics is a handbook for anyone who needs to track and guide software development and delivery at the team level, such as project managers and team leads. New development practices, including "agile" methodologies like Scrum, have redefined which measurements are most meaningful and under what conditions you can benefit from them. This practical book identifies key characteristics of organizational structure, process models, and development methods so that you can select the appropriate metrics for your team. It describes the uses, mechanics, and common abuses of a number of metrics that are useful for steering and for monitoring process improvement. The insights and techniques in this book are based entirely on field experience. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book When driving a car, you are less likely to speed, run out of gas, or suffer engine failure because of the measurements the car reports to you about its condition. Development teams, too, are less likely to fail if they are measuring the parameters that matter to the success of their projects. This book shows you how. Software Development Metrics teaches you how to gather, analyze, and effectively use the metrics that define your organizational structure, process models, and development methods. The insights and examples in this book are based entirely on field experience. You'll learn practical techniques like building tools to track key metrics and developing data-based early warning systems. Along the way, you'll learn which metrics align with different development practices, including traditional and adaptive methods. No formal experience with developing or applying metrics is assumed. What's Inside Identify the most valuable metrics for your team and process Differentiate "improvement" from "change" Learn to interpret and apply the data you gather Common pitfalls and anti-patterns About the Author Dave Nicolette is an organizational transformation consultant, team coach, and trainer. Dave is active in the agile and lean software communities. Table of Contents Making metrics useful Metrics for steering Metrics for improvement Putting the metrics to work Planning predictability Reporting outward and upward
PART I: FUNDAMENTALS OF MEASUREMENT AND EXPERIMENTATION 1. Measurement: What Is It and Why Do It? 2. The Basics of Measurement 3. A Goal-Based Framework for Software Measurement 4. Empirical Investigation 5. Software Metrics Data Collection 6. Analyzing Software-Measurement Data PART II: SOFTWARE-ENGINEERING MEASUREMENT 7. Measuring Internal Product Attributes: Size 8. Measuring Internal Product Attributes: Structure 9. Measuring Internal Product Attributes 10. Software Reliability: Measurement and Prediction 11. Resource Measurement: Productivity, Teams, and Tools 12. Making Process Predictions PART III: MEASUREMENT AND MANAGEMENT 13. Planning a Measurement Program 14. Measurement in Practice 15. Empirical Research in Software Engineering APPENDIXES: A. Solutions to Selected Exercises / B. Metric Tools / C. Acronyms and Glossary / ANNOTATED BIBLIOGRAPHY / INDEX
Winner of the Shingo Publication Award Accelerate your organization to win in the marketplace. How can we apply technology to drive business value? For years, we've been told that the performance of software delivery teams doesn't matter―that it can't provide a competitive advantage to our companies. Through four years of groundbreaking research to include data collected from the State of DevOps reports conducted with Puppet, Dr. Nicole Forsgren, Jez Humble, and Gene Kim set out to find a way to measure software delivery performance―and what drives it―using rigorous statistical methods. This book presents both the findings and the science behind that research, making the information accessible for readers to apply in their own organizations. Readers will discover how to measure the performance of their teams, and what capabilities they should invest in to drive higher performance. This book is ideal for management at every level.
Most of the software measures currently proposed to the industry bring few real benefits to either software managers or developers. This book looks at the classical metrology concepts from science and engineering, using them as criteria to propose an approach to analyze the design of current software measures and then design new software measures (illustrated with the design of a software measure that has been adopted as an ISO measurement standard). The book includes several case studies analyzing strengths and weaknesses of some of the software measures most often quoted. It is meant for software quality specialists and process improvement analysts and managers.
"This book presents theoretical measurement models and metrics for measuring software size and complexity of large scale enterprise resource planning software. It focuses on the relationship between software complexity and quality with the aim of controlling quality and on enterprise resource planning development, performance, challenges, and even success stories"--
The modern field of software metrics emerged from the computer modeling and "statistical thinking" services of the 1980s. As the field evolved, metrics programs were integrated with project management, and metrics grew to be a major tool in the managerial decision-making process of software companies. Now practitioners in the software industry have
This book focuses on a specialized branch of the vast domain of software engineering: component-based software engineering (CBSE). Component-Based Software Engineering: Methods and Metrics enhances the basic understanding of components by defining categories, characteristics, repository, interaction, complexity, and composition. It divides the research domain of CBSE into three major sub-domains: (1) reusability issues, (2) interaction and integration issues, and (3) testing and reliability issues. This book covers the state-of-the-art literature survey of at least 20 years in the domain of reusability, interaction and integration complexities, and testing and reliability issues of component-based software engineering. The aim of this book is not only to review and analyze the previous works conducted by eminent researchers, academicians, and organizations in the context of CBSE, but also suggests innovative, efficient, and better solutions. A rigorous and critical survey of traditional and advanced paradigms of software engineering is provided in the book. Features: In-interactions and Out-Interactions both are covered to assess the complexity. In the context of CBSE both white-box and black-box testing methods and their metrics are described. This work covers reliability estimation using reusability which is an innovative method. Case studies and real-life software examples are used to explore the problems and their solutions. Students, research scholars, software developers, and software designers or individuals interested in software engineering, especially in component-based software engineering, can refer to this book to understand the concepts from scratch. These measures and metrics can be used to estimate the software before the actual coding commences.
Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community's leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid. - Presents the wisdom of community experts, derived from a summit on software analytics - Provides contributed chapters that share discrete ideas and technique from the trenches - Covers top areas of concern, including mining security and social data, data visualization, and cloud-based data - Presented in clear chapters designed to be applicable across many domains
