Measuring the Software Process: Statistical Process Control for Software Process Improvement

by Anita D. Carleton and William A. Florac

Book Overview

""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,... This description may be from another edition of this product.

• Format: Hardcover
• Language: English
• ISBN: 0201604442
• ISBN13: 9780201604443
• Release Date: July 1999
• Publisher: Addison-Wesley Professional
• Length: 272 Pages
• Weight: 0.05 lbs.
• Dimensions: 0.9" x 6.6" x 9.5"

Customer Reviews

Excellent book for CMM level 5 and 6-Sigma organizations

This book contains the keys to meeting core CMM level 5 requirements, which defines key processes for optimizing and continuous improvement, and for achieving 6-sigma processes. However, you need not be striving for either (or both) of these goals to use the techniques and approach in this book to full advantage.Implementing and employing statistical process controls are the basis of this book. The authors lead you through the steps and techniques necessary to implement and use SPC, starting with background information on processes and a process measurement framework, and moving through topics such as planning your measurement strategy, data collection and analysis, and developing and interpreting process behavior charts using common SPC chart types. The most common controls are x-bar (mean) and r (range) charts. Be aware that any SPC approach requires two conditions to be met: (a) defined processes, and (b) the processes are in statistical control (meaning that the data points being measured have settled into a normal distribution that are randomly clustered around a mean and have defined upper and lower control limits). New processes, or processes that are not managed well enough to have these characteristics are not candidates for SPC. This book requires knowledge and skills in basic statistical analysis. If you require a refresher I recommend reading "Visual Statistics" by Jack R. Fraenkel before tackling this book. I also recommend "Applied Statistics for Software Managers" by Katrina Maxwell, which not only teaches the basics, but also approaches measurement from the perspective of multi-variable analysis, regression analysis and other basic measurement techniques, which nicely complements the SPC material in this book and gives a broader picture of metrics.

First book of its kind.

This book is a self-contained statistical process control (SPC) foundation in the context of software process improvement. Authors Florac and Carleton apply early industrial wisdom and some previous work at the SEI to a modern software development environment. The emphasis of the book is primarily on the use of analytical studies (predicting future outcomes) using the control chart as the primary instrument. There is only brief treatment of the use of enumerative studies (evaluating current situations) in this problem domain. Time-honored tools such as the Pareto chart, cause and effect diagram, and histogram, however, are given much less attention. The first half of the book directs attention to critical topics such as planning, managing and measuring. The authors adequately cover the material as it applies to software development, but the reader is cautioned that many statistical fundamentals are omitted from this work. In order to put these ideas into practice one should seek further instruction or consult a statistician for best results. A few annoying typographical and redundancy errors are present as well. Most bothersome about the book is that the authors do not seem to be 100% convinced that SPC for software process improvement actually works! This is somewhat alarming, given the long successful history of SPC in other industries. All in all, however, this book desperately needs to be read by anyone wishing to improve a software development process.

A Practical Guidance

This book gives a practical guidance on software process measurement: what should be measured, how to measure, the measurement process/procedure, the data analysis of measurement, and the application of analysed results. It's easy to read and understand. It would be better to include more "case study" information.