Quantifying Software
Global and Industry Perspectives
Published on 24. October 2017
533 pages
978-1-351-17635-4 (ISBN)
Description
Software is one of the most important products in human history and is widely used by all industries and all countries. It is also one of the most expensive and labor-intensive products in human history. Software also has very poor quality that has caused many major disasters and wasted many millions of dollars. Software is also the target of frequent and increasingly serious cyber-attacks.
Among the reasons for these software problems is a chronic lack of reliable quantified data. This reference provides quantified data from many countries and many industries based on about 26,000 projects developed using a variety of methodologies and team experience levels. The data has been gathered between 1970 and 2017, so interesting historical trends are available.
Since current average software productivity and quality results are suboptimal, this book focuses on "best in class" results and shows not only quantified quality and productivity data from best-in-class organizations, but also the technology stacks used to achieve best-in-class results. The overall goal of this book is to encourage the adoption of best-in-class software metrics and best-in-class technology stacks. It does so by providing current data on average software schedules, effort, costs, and quality for several industries and countries.
Because productivity and quality vary by technology and size, the book presents quantitative results for applications between 100 function points and 100,000 function points. It shows quality results using defect potential and DRE metrics because the number one cost driver for software is finding and fixing bugs. The book presents data on cost of quality for software projects and discusses technical debt, but that metric is not standardized. Finally, the book includes some data on three years of software maintenance and enhancements as well as some data on total cost of ownership.
Among the reasons for these software problems is a chronic lack of reliable quantified data. This reference provides quantified data from many countries and many industries based on about 26,000 projects developed using a variety of methodologies and team experience levels. The data has been gathered between 1970 and 2017, so interesting historical trends are available.
Since current average software productivity and quality results are suboptimal, this book focuses on "best in class" results and shows not only quantified quality and productivity data from best-in-class organizations, but also the technology stacks used to achieve best-in-class results. The overall goal of this book is to encourage the adoption of best-in-class software metrics and best-in-class technology stacks. It does so by providing current data on average software schedules, effort, costs, and quality for several industries and countries.
Because productivity and quality vary by technology and size, the book presents quantitative results for applications between 100 function points and 100,000 function points. It shows quality results using defect potential and DRE metrics because the number one cost driver for software is finding and fixing bugs. The book presents data on cost of quality for software projects and discusses technical debt, but that metric is not standardized. Finally, the book includes some data on three years of software maintenance and enhancements as well as some data on total cost of ownership.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
File size
2,03 MB
ISBN-13
978-1-351-17635-4 (9781351176354)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
Capers Jones is currently vice president and chief technology officer of Namcook Analytics LLC (www.Namcook.com). Namcook Analytics LLC designs leading edge risk, cost, and quality estimation and measurement tools. Software Risk Master (SRM) is the company's advanced estimation tool with a patent-pending early sizing feature that allows sizing before requirements via pattern matching. Namcook Analytics also collects software benchmark data and engages in longer range software process improvement, quality, and risk assessment studies. These Namcook studies are global and involve major corporations and some government agencies in many countries in Europe, Asia, and South America.
Content
1. Introduction to Quantifying Software Results
Software Revenue Generation
Operating Cost Reductions
Market Expansion.
2. The Origin and Evolution of Function Point Metrics.
The Origins of Function Point Metrics at IBM
New and Old Function Point Business Models
The Costs and Limitations of Standard Function Point Metrics
Expanding the Role and Advancing the Start Time of Function Point Analysis
The Current Business Model of Function Point Analysis in the United States.
A New Business Model for Function Point Analysis
The Hazards and Errors of LOC Metrics
A Short History of LOC Metrics
The Hazards and Errors of the Cost per Defect Metric
The Hazards of Multiple Metrics without Conversion Rules
Extending Function Point Logic into New Domains
Potential Expansion of Function Points to Other Business Topics
Example of Multi-Metric Software Economic Analysis
The Probable Effort and Skill Sets to Create Additional Metrics
Size and Cost Growth over Multiple-Year periods
3. Software Information Needed by Corporate Executives
Answers to the 60 Software Questions
Primary Software Metrics for High Precision
Supplemental Software Metrics for High Precision
Answers to the Current "Hot Topic" Questions
Answers to the Security, Quality, and Governance Questions
Answers to the Software Usage, Value, and User Satisfaction Questions
Answers to the Employee Satisfaction and Demographic Questions
Answers to the Software Economic Impact Questions
Answers to the Competitive Analysis Questions
Twenty-Five Quantitative Software Engineering Targets
Technologies Useful in Achieving Software Engineering Goals
Six Hazardous Software Engineering Methods to be Avoided
4. Metrics to Solve Problems and Improve Software Engineering Quality and Productivity
Reducing Software Wastage
Reuse of Certified Materials for Software Projects
Achieving Excellence in Software Quality Control
Excellent Quality Control
Average Quality Control
Poor Quality Control
Metrics to Improve Software Quality
Software Quality and Software Security
Software Quality and Technical Debt
SNAP Metrics for Nonfunctional Size
Economic Value of High Software Quality
A Primer on Manufacturing Economics and the Impact of Fixed Costs
Software's Lack of Accurate Data and Poor Education on Quality and Cost of Quality
Summary and Conclusions on Metrics for Problem-Solving
Improving Software Project Management Tools and Training
Project Management Knowledge Acquisition.
The History of Software Project Management Tools
Usage Patterns of Software Project Management Tools
Recent Evolution of Software Project Management Tools
The Costs and Value of Software Project Management Tools
The Future of Software Project Management Tools
5. Measures, Metrics, and Management
Improving Software Project Management Tools and Training
Summary and Conclusions on Software Project Management
6. 50 Years of Global Software Benchmark Results
Measuring U.S. Software Productivity and Quality
Life Expectancy of Software Benchmark Data
U.S. Software Benchmark Results
Software Cost Drivers
Phase-Based Costs versus Activity-Based Costs
The Strange Mystery of Why Software Has 3000 Programming Languages
U.S. Industry Work Hour Variations
U.S. Industry Productivity and Quality Results circa 2017
Comparing Software Globally
Topics that Cannot Be in Benchmarks due to Laws or Union Regulations
Examples of Three Software Benchmarks
Benchmark Section 0: Executive Summary
Benchmark Section 1: Input Data
Benchmark Section 2: Output Data Schedule, Effort, and Cost Benchmark Data
Benchmark Section 3: Technology Stack Evaluation
Benchmark Section 4: Topics Observed During Benchmark Process
Benchmark Section 5: Maintenance, Enhancement, and Support Benchmarks
7. Advancing Software Benchmark Technology
Synthetic Benchmarks Using Parametric Estimation to Improve
Speed and Accuracy
Measuring Brand New Tools, Methods, and Programming Languages
Executive Interest Levels in Software Benchmark Types
Software Benchmark Providers
Software Revenue Generation
Operating Cost Reductions
Market Expansion.
2. The Origin and Evolution of Function Point Metrics.
The Origins of Function Point Metrics at IBM
New and Old Function Point Business Models
The Costs and Limitations of Standard Function Point Metrics
Expanding the Role and Advancing the Start Time of Function Point Analysis
The Current Business Model of Function Point Analysis in the United States.
A New Business Model for Function Point Analysis
The Hazards and Errors of LOC Metrics
A Short History of LOC Metrics
The Hazards and Errors of the Cost per Defect Metric
The Hazards of Multiple Metrics without Conversion Rules
Extending Function Point Logic into New Domains
Potential Expansion of Function Points to Other Business Topics
Example of Multi-Metric Software Economic Analysis
The Probable Effort and Skill Sets to Create Additional Metrics
Size and Cost Growth over Multiple-Year periods
3. Software Information Needed by Corporate Executives
Answers to the 60 Software Questions
Primary Software Metrics for High Precision
Supplemental Software Metrics for High Precision
Answers to the Current "Hot Topic" Questions
Answers to the Security, Quality, and Governance Questions
Answers to the Software Usage, Value, and User Satisfaction Questions
Answers to the Employee Satisfaction and Demographic Questions
Answers to the Software Economic Impact Questions
Answers to the Competitive Analysis Questions
Twenty-Five Quantitative Software Engineering Targets
Technologies Useful in Achieving Software Engineering Goals
Six Hazardous Software Engineering Methods to be Avoided
4. Metrics to Solve Problems and Improve Software Engineering Quality and Productivity
Reducing Software Wastage
Reuse of Certified Materials for Software Projects
Achieving Excellence in Software Quality Control
Excellent Quality Control
Average Quality Control
Poor Quality Control
Metrics to Improve Software Quality
Software Quality and Software Security
Software Quality and Technical Debt
SNAP Metrics for Nonfunctional Size
Economic Value of High Software Quality
A Primer on Manufacturing Economics and the Impact of Fixed Costs
Software's Lack of Accurate Data and Poor Education on Quality and Cost of Quality
Summary and Conclusions on Metrics for Problem-Solving
Improving Software Project Management Tools and Training
Project Management Knowledge Acquisition.
The History of Software Project Management Tools
Usage Patterns of Software Project Management Tools
Recent Evolution of Software Project Management Tools
The Costs and Value of Software Project Management Tools
The Future of Software Project Management Tools
5. Measures, Metrics, and Management
Improving Software Project Management Tools and Training
Summary and Conclusions on Software Project Management
6. 50 Years of Global Software Benchmark Results
Measuring U.S. Software Productivity and Quality
Life Expectancy of Software Benchmark Data
U.S. Software Benchmark Results
Software Cost Drivers
Phase-Based Costs versus Activity-Based Costs
The Strange Mystery of Why Software Has 3000 Programming Languages
U.S. Industry Work Hour Variations
U.S. Industry Productivity and Quality Results circa 2017
Comparing Software Globally
Topics that Cannot Be in Benchmarks due to Laws or Union Regulations
Examples of Three Software Benchmarks
Benchmark Section 0: Executive Summary
Benchmark Section 1: Input Data
Benchmark Section 2: Output Data Schedule, Effort, and Cost Benchmark Data
Benchmark Section 3: Technology Stack Evaluation
Benchmark Section 4: Topics Observed During Benchmark Process
Benchmark Section 5: Maintenance, Enhancement, and Support Benchmarks
7. Advancing Software Benchmark Technology
Synthetic Benchmarks Using Parametric Estimation to Improve
Speed and Accuracy
Measuring Brand New Tools, Methods, and Programming Languages
Executive Interest Levels in Software Benchmark Types
Software Benchmark Providers
