Measuring the Software Process

William A. Florac, Anita D. Carleton

  • 出版商: Addison Wesley
  • 出版日期: 1999-07-25
  • 售價: $2,310
  • 貴賓價: 9.5$2,195
  • 語言: 英文
  • 頁數: 272
  • 裝訂: Hardcover
  • ISBN: 0201604442
  • ISBN-13: 9780201604443
  • 立即出貨 (庫存 < 4)




"While it is usually helpful to launch improvement programs, many such programs soon get bogged down in detail. They either address the wrong problems, or they keep beating on the same solutions, wondering why things don't improve. This is when you need an objective way to look at the problems. This is the time to get some data."
Watts S. Humphrey, from the Foreword

This book, drawing on work done at the Software Engineering Institute and other organizations, shows how to use measurements to manage and improve software processes. The authors explain specifically how quality characteristics of software products and processes can be quantified, plotted, and analyzed so the performance of software development activities can be predicted, controlled, and guided to achieve both business and technical goals. The measurement methods presented, based on the principles of statistical quality control, are illuminated by application examples taken from industry.

Although many of the methods discussed are applicable to individual projects, the book's primary focus is on the steps software development organizations can take toward broad-reaching, long-term success. The book particularly addresses the needs of software managers and practitioners who have already set up some kind of basic measurement process and are ready to take the next step by collecting and analyzing software data as a basis for making process decisions and predicting process performance.

Highlights of the book include:

  • Insight into developing a clear framework for measuring process behavior
  • Discussions of process performance, stability, compliance, capability, and improvement
  • Explanations of what you want to measure (and why) and instructions on how to collect your data
  • Step-by-step guidance on how to get started using statistical process control

If you have responsibilities for product quality or process performance and you are ready to use measurements to manage, control, and predict your software processes, this book will be an invaluable resource.


Table of Contents





1. Managing and Measuring Process Behavior.

What Is a Software Process?

What Is Software Process Management?

The Role of Software Process Management.

Issues on the Road to Process Improvement.

The Need for Software Process Measurement.

Measuring Process Behavior.

A Framework for Process Behavior Measurement.


2. Planning for Measurement.

Identifying Process Issues.

Selecting and Defining Measures.

Integrating Measures with the Software Process.


3. Collecting the Data.

Principal Tasks.

The Specifics of Collecting Software Process Data.

Reviewing and Assessing Collected Data.

Retaining Data.

Tools for Understanding Your Data.


4. Analyzing Process Behavior.

Separating Signals from Noise.

Evaluating Process Stability.

Control Chart Basics.


5. Process Behavior Charts for Software Processes.

Control Charts for Variables or Discrete Data.

Control Charts for Attributes Data.


6. More About Process Behavior Charts.

How Much Data Is Enough?

Anomalous Process Behavior Patterns.

Rational Sampling and Homogeneity of Subgroups.

Rational Subgrouping.

The Problem of Insufficient Granularity in Recorded Values.

Aggregation and Decomposition of Process Performance Data.


7. Three Paths to Process Improvement.

Finding and Correcting Assignable Causes.

Process Capability.

Process Capability Analysis.

Improving the Process.

Improvement and Investment.


8. Getting Started.

Ten Steps for Getting Started.

Frequently Asked Questions Regarding SPC.

Final Remarks.

Appendix A Control Chart Tables and Formulas.

Appendix B More About Analyzing Process Behavior.

Enumerative Versus Analytic Studies.

Three-Sigma Control Limits.

Central Limit Theorem and Role of the Normal Distribution.

Appendix C. Example Data and Calculations.

Appendix C.1.

Appendix C.2.