Estimating Software-Intensive Systems : Projects, Products, and Processes

Many software projects fail because their leaders don't know how to estimate, schedule, or measure them accurately.

Fortunately, proven tools and techniques exist for every facet of software estimation.

Estimating Software-Intensive Systems brings them together in a real-world guidebook that will help software managers, engineers, and customers immediately improve their estimates–and drive continuing improvements over time.   Dick Stutzke presents here a disciplined and repeatable process that can produce accurate and complete estimates for any project, product, or process, no matter how new or unusual.

Stutzke doesn't just describe formal techniques: He offers simple, easy-to-use templates, spreadsheets, and tools you can start using today to identify and estimate product size, performance, and quality–as well as project cost, schedule, and risk reserves.   Stutzke shows how to quickly "get your arms around" users' problems and requirements, the structure of a solution, and the process needed to deliver it.

You'll learn how to choose the most appropriate estimating techniques and tools; collect accurate data, track progress, and update estimates; and recalibrate estimating models to improve estimation accuracy.   Stutzke's techniques apply whether you're creating custom in-house business software, purchasing or customizing "off-the-shelf" technology, or constructing complex, one-of-a-kind military, industrial, or commercial systems.

These techniques apply to small and large projects, and to all project life cycles–from agile to plan-driven.

This book will help you plan, estimate, budget, schedule, purchase, design, build, test, deploy, operate, and maintain software-intensive systems.

It explains how to size software, identify all cost components, calculate the associated costs, and set a competitive price.   A separate section covers topics of interest for large projects: designing an appropriate work breakdown structure, collecting data from cost accounting systems, and using earned value measurement.