کمیت‌سنجی نرم‌افزار: دیدگاه‌های جهانی و صنعتی

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. MuPDF error: syntax error: invalid key in dict MuPDF error: syntax error: invalid key in dict MuPDF error: syntax error: invalid key in dict MuPDF error: syntax error: invalid key in dict MuPDF error: syntax error: invalid key in dict MuPDF error: syntax error: invalid key in dict MuPDF error: syntax error: invalid key in dict MuPDF error: syntax error: invalid key in dict MuPDF error: syntax error: invalid key in dict Cover 1 Half Title 2 Title Page 4 Copyright Page 5 Contents 6 Preface 12 Acknowledgments 26 Author 28 1 Introduction to Quantifying Software Results 30 Software Revenue Generation 133 Operating Cost Reductions 134 Market Expansion 134 Summary and Conclusions on the 50 Software Economic Factors 149 References 150 2 The Origin and Evolution of Function Point Metrics 154 The Origins of Function Point Metrics at IBM 155 New and Old Function Point Business Models 159 The Costs and Limitations of Standard Function Point Metrics 161 Expanding the Role and Advancing the Start Time of Function Point Analysis 164 The Current Business Model of Function Point Analysis in the United States 167 A New Business Model for Function Point Analysis 170 The Hazards and Errors of LOC Metrics 192 Case 1: Application Written in the Assembly Language 193 Case 2: The Same Application Written in the C++ Language 194 A Short History of LOC Metrics 194 The Hazards and Errors of the Cost per Defect Metric 198 Case A: Poor Quality 198 Case B: Good Quality 198 Case C: Zero Defects 199 The Hazards of Multiple Metrics without Conversion Rules 199 Extending Function Point Logic into New Domains 202 Potential Expansion of Function Points to Other Business Topics 203 Topic 1: The Need for Application Function Point Metrics 204 Topic 2: The Need for Component Feature Point Metrics 205 Topic 3: The Need for Hardware Function Point Metrics 207 Topic 4: The Need for COTS Function Point Metrics 208 Topic 5: The Need for Micro Function Point Metrics 210 Topic 6: The Need for Data Point Metrics 212 Topic 7: The Need for Website Point Metrics 214 Topic 8: The Need for Software Usage Point Metrics 214 Topic 9: The Need for Service Point Metrics 217 Topic 10: The Need for Value Point Metrics 218 Topic 11: The Need for Risk Point Metrics 219 Topic 12: The Need for Security Points 220 Topic 13: The Need for Configuration Points 221 Topic 14: The Need for Nonfunctional Size (SNAP Points) 222 Example of Multi-Metric Software Economic Analysis 222 The Probable Effort and Skill Sets to Create Additional Metrics 224 Size and Cost Growth over Multiple-Year Periods 227 Summary and Conclusions on Function Points and Expanded Functional Metrics 228 Readings and References on Metrics and Function Point Analysis 229 3 Software Information Needed by Corporate Executives 232 Answers to the 60 Software Questions 236 Primary Software Metrics for High Precision 236 Supplemental Software Metrics for High Precision 237 Answers to the Current “Hot Topic” Questions 237 Answers to the Security, Quality, and Governance Questions 242 Answers to the Software Usage, Value, and User Satisfaction Questions 242 Answers to the Employee Satisfaction and Demographic Questions 243 Answers to the Software Economic Impact Questions 246 Answers to the Competitive Analysis Questions 247 Twenty-Five Quantitative Software Engineering Targets 249 Technologies Useful in Achieving Software Engineering Goals 256 Six Hazardous Software Engineering Methods to Be Avoided 257 References and Readings 258 4 Metrics to Solve Problems and Improve Software Engineering Quality and Productivity 262 Reducing Software Wastage 263 Reuse of Certified Materials for Software Projects 271 Achieving Excellence in Software Quality Control 272 Excellent Quality Control 272 Average Quality Control 273 Poor Quality Control 274 Metrics to Improve Software Quality 280 Software Quality and Software Security 303 Software Quality and Technical Debt 304 SNAP Metrics for Nonfunctional Size 305 Economic Value of High Software Quality 308 A Primer on Manufacturing Economics and the Impact of Fixed Costs 308 Software’s Lack of Accurate Data and Poor Education on Quality and Cost of Quality 316 Summary and Conclusions on Metrics for Problem-Solving 318 Improving Software Project Management Tools and Training 318 Project Management Knowledge Acquisition 324 The History of Software Project Management Tools 327 Usage Patterns of Software Project Management Tools 329 Recent Evolution of Software Project Management Tools 331 The Costs and Value of Software Project Management Tools 342 The Future of Software Project Management Tools 343 References and Readings on Software Issues 345 5 Measures, Metrics, and Management 350 Introduction 350 Improving Software Project Management Tools and Training 352 Initial Education for New Project Managers 352 Continuing Education for Software Project Managers 355 Guest Lectures from Visiting Experts 359 Acquisition and Use of Software Parametric Estimation Tools 359 Acquisition and Use of Progress and Milestone Tracking Tools 363 The Use of Formal Project Offices for Applications >1000 Function Points 364 Use and Measurement of Effective Quality Control Methods 365 Elimination of Bad Metrics and Adoption of Effective Software Metrics 372 Primary Software Metrics for High Precision 375 Supplemental Software Metrics for High Precision 375 Commissioning Annual Software Benchmark Studies 377 Formal Best Practice Analysis of Software Tools, Methods, and Quality 378 Summary and Conclusions on Software Project Management 386 Metrics and Measures for Achieving Software Excellence 387 Software Sizing, Estimation, and Project Tracking Differences 390 Software Quality Differences for Best, Average, and Poor Projects 391 Excellent Quality Control 394 Average Quality Control 394 Poor Quality Control 395 Reuse of Certified Materials for Software Projects 397 Software Methodologies 400 Quantifying Software Excellence 403 The Metaphor of Technical Debt 404 Stages in Achieving Software Excellence 406 Stage 1: Quantify Your Current Software Results 406 Stage 2: Begin to Adopt State-of-the-Art Quality Tools and Methods 407 Stage 3: Continuous Improvements Forever 409 Going beyond Stage 3 into Formal Reuse Programs 411 Summary and Conclusions 411 Suggested Readings on Software Project Management 411 Suggested Websites 414 6 50 Years of Global Software Benchmark Results 418 Introduction 418 Measuring U.S. Software Productivity and Quality 419 Life Expectancy of Software Benchmark Data 421 U.S. Software Benchmark Results 427 Software Cost Drivers 433 Phase-Based Costs versus Activity-Based Costs 435 The Strange Mystery of Why Software Has 3000 Programming Languages 439 U.S. Industry Work Hour Variations 452 U.S. Industry Productivity and Quality Results circa 2017 454 Comparing Software Globally 462 Topics That Cannot Be in Benchmarks due to Laws or Union Regulations 485 Examples of Three Software Benchmarks 487 Benchmark Section 0: Executive Summary 488 Benchmark Section 1: Input Data 490 Benchmark Section 2: Output Data 496 Sizing and Other Quantitative Topics Including Risk Assessments 499 Software Quality Benchmark Data 504 Schedule, Effort, and Cost Benchmark Data 508 Benchmark Section 3: Technology Stack Evaluation 513 Benchmark Section 4: Topics Observed during Benchmark Process 517 Benchmark Section 5: Maintenance, Enhancement, and Support Benchmarks 531 Summary and Conclusions on Global Benchmarks 531 7 Advancing Software Benchmark Technology 534 Synthetic Benchmarks Using Parametric Estimation to Improve Speed and Accuracy 534 Measuring Brand New Tools, Methods, and Programming Languages 537 Executive Interest Levels in Software Benchmark Types 539 Software Benchmark Providers 539 Summary and Conclusions on Estimation, Benchmarks, and Quantified Results 539 References and Readings on Software Costs 546 Index 550 Software measurement practices are so bad that the industry has very sparse reliable data on software schedules, costs, quality, and productivity. Much of the available data is incorrect, incomplete, or both. This book provides quantified software results by country, by industry, by technology, by application size, and by other factors.