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E-raamat: Quantifying Software: Global and Industry Perspectives

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(Software Productivity Research, Inc., Massachusetts, USA)
  • Formaat: 561 pages
  • Ilmumisaeg: 24-Oct-2017
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9781000740561
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Quantifying Software: Global and Industry PerspectivesSuurem pilt
  • Formaat: 561 pages
  • Ilmumisaeg: 24-Oct-2017
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9781000740561
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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.
Preface xi
Acknowledgments xxv
Author xxvii
1 Introduction to Quantifying Software Results
1(124)
Software Revenue Generation
104(1)
Operating Cost Reductions
105(1)
Market Expansion
105(15)
Summary and Conclusions on the 50 Software Economic Factors
120(1)
References
121(4)
2 The Origin and Evolution of Function Point Metrics
125(78)
The Origins of Function Point Metrics at IBM
126(4)
New and Old Function Point Business Models
130(2)
The Costs and Limitations of Standard Function Point Metrics
132(3)
Expanding the Role and Advancing the Start Time of Function Point Analysis
135(3)
The Current Business Model of Function Point Analysis in the United States
138(3)
A New Business Model for Function Point Analysis
141(22)
The Hazards and Errors of LOC Metrics
163(2)
Case 1 Application Written in the Assembly Language
164(1)
Case 2 The Same Application Written in the C++ Language
165(1)
A Short History of LOC Metrics
165(4)
The Hazards and Errors of the Cost per Defect Metric
169(1)
Case A Poor Quality
169(1)
Case B Good Quality
169(1)
Case C Zero Defects
170(1)
The Hazards of Multiple Metrics without Conversion Rules
170(3)
Extending Function Point Logic into New Domains
173(1)
Potential Expansion of Function Points to Other Business Topics
174(19)
Topic 1 The Need for Application Function Point Metrics
175(1)
Topic 2 The Need for Component Feature Point Metrics
176(2)
Topic 3 The Need for Hardware Function Point Metrics
178(1)
Topic 4 The Need for COTS Function Point Metrics
179(2)
Topic 5 The Need for Micro Function Point Metrics
181(2)
Topic 6 The Need for Data Point Metrics
183(2)
Topic 7 The Need for Website Point Metrics
185(1)
Topic 8 The Need for Software Usage Point Metrics
185(3)
Topic 9 The Need for Service Point Metrics
188(1)
Topic 10 The Need for Value Point Metrics
189(1)
Topic 11 The Need for Risk Point Metrics
190(1)
Topic 12 The Need for Security Points
191(1)
Topic 13 The Need for Configuration Points
192(1)
Topic 14 The Need for Nonfunctional Size (SNAP Points)
193(1)
Example of Multi-Metric Software Economic Analysis
193(2)
The Probable Effort and Skill Sets to Create Additional Metrics
195(3)
Size and Cost Growth over Multiple-Year Periods
198(1)
Summary and Conclusions on Function Points and Expanded Functional Metrics
199(1)
Readings and References on Metrics and Function Point Analysis
200(3)
3 Software Information Needed by Corporate Executives
203(30)
Answers to the 60 Software Questions
207(1)
Primary Software Metrics for High Precision
207(1)
Supplemental Software Metrics for High Precision
208(1)
Answers to the Current "Hot Topic" Questions
208(5)
Answers to the Security, Quality, and Governance Questions
213(1)
Answers to the Software Usage, Value, and User Satisfaction Questions
213(1)
Answers to the Employee Satisfaction and Demographic Questions
214(3)
Answers to the Software Economic Impact Questions
217(1)
Answers to the Competitive Analysis Questions
218(2)
Twenty-Five Quantitative Software Engineering Targets
220(7)
Technologies Useful in Achieving Software Engineering Goals
227(1)
Six Hazardous Software Engineering Methods to Be Avoided
228(1)
References and Readings
229(4)
4 Metrics to Solve Problems and Improve Software Engineering Quality and Productivity
233(88)
Reducing Software Wastage
234(8)
Reuse of Certified Materials for Software Projects
242(1)
Achieving Excellence in Software Quality Control
243(1)
Excellent Quality Control
243(1)
Average Quality Control
244(1)
Poor Quality Control
245(6)
Metrics to Improve Software Quality
251(23)
Software Quality and Software Security
274(1)
Software Quality and Technical Debt
275(1)
SNAP Metrics for Nonfunctional Size
276(3)
Economic Value of High Software Quality
279(1)
A Primer on Manufacturing Economics and the Impact of Fixed Costs
279(8)
Software's Lack of Accurate Data and Poor Education on Quality and Cost of Quality
287(2)
Summary and Conclusions on Metrics for Problem-Solving
289(1)
Improving Software Project Management Tools and Training
289(6)
Project Management Knowledge Acquisition
295(3)
The History of Software Project Management Tools
298(2)
Usage Patterns of Software Project Management Tools
300(2)
Recent Evolution of Software Project Management Tools
302(11)
The Costs and Value of Software Project Management Tools
313(1)
The Future of Software Project Management Tools
314(2)
References and Readings on Software Issues
316(5)
5 Measures, Metrics, and Management
321(68)
Introduction
321(2)
Improving Software Project Management Tools and Training
323(1)
Initial Education for New Project Managers
323(3)
Continuing Education for Software Project Managers
326(4)
Guest Lectures from Visiting Experts
330(1)
Acquisition and Use of Software Parametric Estimation Tools
330(4)
Acquisition and Use of Progress and Milestone Tracking Tools
334(1)
The Use of Formal Project Offices for Applications > 1000 Function Points
335(1)
Use and Measurement of Effective Quality Control Methods
336(7)
Elimination of Bad Metrics and Adoption of Effective Software Metrics
343(3)
Primary Software Metrics for High Precision
346(1)
Supplemental Software Metrics for High Precision
346(2)
Commissioning Annual Software Benchmark Studies
348(1)
Formal Best Practice Analysis of Software Tools, Methods, and Quality
349(8)
Summary and Conclusions on Software Project Management
357(1)
Metrics and Measures for Achieving Software Excellence
358(3)
Software Sizing, Estimation, and Project Tracking Differences
361(1)
Software Quality Differences for Best, Average, and Poor Projects
362(3)
Excellent Quality Control
365(1)
Average Quality Control
365(1)
Poor Quality Control
366(2)
Reuse of Certified Materials for Software Projects
368(3)
Software Methodologies
371(3)
Quantifying Software Excellence
374(1)
The Metaphor of Technical Debt
375(2)
Stages in Achieving Software Excellence
377(5)
Stage 1 Quantify Your Current Software Results
377(1)
Stage 2 Begin to Adopt State-of-the-Art Quality Tools and Methods
378(2)
Stage 3 Continuous Improvements Forever
380(2)
Going beyond Stage 3 into Formal Reuse Programs
382(1)
Summary and Conclusions
382(1)
Suggested Readings on Software Project Management
382(3)
Suggested Websites
385(4)
6 50 Years of Global Software Benchmark Results
389(116)
Introduction
389(1)
Measuring U.S. Software Productivity and Quality
390(2)
Life Expectancy of Software Benchmark Data
392(6)
U.S. Software Benchmark Results
398(6)
Software Cost Drivers
404(2)
Phase-Based Costs versus Activity-Based Costs
406(4)
The Strange Mystery of Why Software Has 3000 Programming Languages
410(13)
U.S. Industry Work Hour Variations
423(2)
U.S. Industry Productivity and Quality Results circa 2017
425(8)
Comparing Software Globally
433(23)
Topics That Cannot Be in Benchmarks due to Laws or Union Regulations
456(2)
Examples of Three Software Benchmarks
458(1)
Benchmark Section 0: Executive Summary
459(2)
Benchmark Section 1: Input Data
461(6)
Benchmark Section 2: Output Data
467(3)
Sizing and Other Quantitative Topics Including Risk Assessments
470(5)
Software Quality Benchmark Data
475(4)
Schedule, Effort, and Cost Benchmark Data
479(5)
Benchmark Section 3: Technology Stack Evaluation
484(4)
Benchmark Section 4: Topics Observed during Benchmark Process
488(14)
Benchmark Section 5: Maintenance, Enhancement, and Support Benchmarks
502(1)
Summary and Conclusions on Global Benchmarks
502(3)
7 Advancing Software Benchmark Technology
505(16)
Synthetic Benchmarks Using Parametric Estimation to Improve Speed and Accuracy
505(3)
Measuring Brand New Tools, Methods, and Programming Languages
508(2)
Executive Interest Levels in Software Benchmark Types
510(1)
Software Benchmark Providers
510(1)
Summary and Conclusions on Estimation, Benchmarks, and Quantified Results
510(7)
References and Readings on Software Costs
517(4)
Index 521
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 companys 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.
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