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Measuring the Software Process: Statistical Process Control for Software Process Improvement
"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 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.
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 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.
250 pages, Hardcover
First published July 15, 1999
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Displaying 1 - 2 of 2 reviews
December 24, 2020
The benefit of the chapters would more likely be useful, in the language the're presented, when used as a reference guide at some later stage, however the material is difficult to reference in the future without bookmarking.
I just don't think the author's presentation of materials matches the format and layout of the book.
I just don't think the author's presentation of materials matches the format and layout of the book.
February 29, 2016
Contrary to most books in computer science, this one has remained very topical to the modern world of software development. Even though it was published in 1999, the strategies put forward can be applied today. Furthermore, the content can be horizontally applied across a wide variety of disciplines.
This is due to the fact that what is developed in this book is a set of tactics that can be applied to nearly every development process. The subtitle could have been “Statistical Process Control for Process Improvement.” While there are significant differences between software development and other creative processes, much of what is done in quality control is identical across disciplines.
It all starts with determining if you have a system that has enough stability so that it makes sense to even attempt to measure it, at least in the statistical sense. There is a lot that can be done with statistics, but most of it is based on assumptions that what has happened so far is an accurate rendition of what will happen in the future.
Once that is established, and doing that is explained, the next tactics are collecting and evaluating the data. Charts and other visual aids are used to not only explain trends, but also to demonstrate how one works and analyzes data that will naturally contain a lot of normal variation and some that is abnormal.
This is not a book that one can simply hand to anybody and say, “We need to do this.” To understand and implement the content of this book it is necessary to have a basic understanding of statistical processes. On the positive side, the standard college course in basic statistics will generally be sufficient.
One of the best sentences that sums up one of the problems with working with all such processes is the title of section 6.1 “How Much Data Is Enough?” Collecting data is like hiking across the American prairie. You know that you have to stop at some point, but you never know if the ideal spot is just over the rise you see in the distance.
There is a famous quote that sums up many of the problems of effective quality control.
“Not everything that counts can be counted, and not everything that can be counted counts.”
After reading the book, this quote will be less applicable to your professional life.
This is due to the fact that what is developed in this book is a set of tactics that can be applied to nearly every development process. The subtitle could have been “Statistical Process Control for Process Improvement.” While there are significant differences between software development and other creative processes, much of what is done in quality control is identical across disciplines.
It all starts with determining if you have a system that has enough stability so that it makes sense to even attempt to measure it, at least in the statistical sense. There is a lot that can be done with statistics, but most of it is based on assumptions that what has happened so far is an accurate rendition of what will happen in the future.
Once that is established, and doing that is explained, the next tactics are collecting and evaluating the data. Charts and other visual aids are used to not only explain trends, but also to demonstrate how one works and analyzes data that will naturally contain a lot of normal variation and some that is abnormal.
This is not a book that one can simply hand to anybody and say, “We need to do this.” To understand and implement the content of this book it is necessary to have a basic understanding of statistical processes. On the positive side, the standard college course in basic statistics will generally be sufficient.
One of the best sentences that sums up one of the problems with working with all such processes is the title of section 6.1 “How Much Data Is Enough?” Collecting data is like hiking across the American prairie. You know that you have to stop at some point, but you never know if the ideal spot is just over the rise you see in the distance.
There is a famous quote that sums up many of the problems of effective quality control.
“Not everything that counts can be counted, and not everything that can be counted counts.”
After reading the book, this quote will be less applicable to your professional life.
Displaying 1 - 2 of 2 reviews