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E-grāmata: Guide to Selecting Software Measures and Metrics

(Software Productivity Research, Inc., Massachusetts, USA)
  • Formāts: 372 pages
  • Izdošanas datums: 03-Mar-2017
  • Izdevniecība: CRC Press
  • Valoda: eng
  • ISBN-13: 9781315314624
  • Formāts - EPUB+DRM
  • Cena: 135,25 €*
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  • Formāts: 372 pages
  • Izdošanas datums: 03-Mar-2017
  • Izdevniecība: CRC Press
  • Valoda: eng
  • ISBN-13: 9781315314624

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Going where no book on software measurement and metrics has previously gone, this critique thoroughly examines a number of bad measurement practices, hazardous metrics, and huge gaps and omissions in the software literature that neglect important topics in measurement. The book covers the major gaps and omissions that need to be filled if data about software development is to be useful for comparisons or estimating future projects.

Among the more serious gaps are leaks in reporting about software development efforts that, if not corrected, can distort data and make benchmarks almost useless and possibly even harmful. One of the most common leaks is that of unpaid overtime. Software is a very labor-intensive occupation, and many practitioners work very long hours. However, few companies actually record unpaid overtime. This means that software effort is underreported by around 15%, which is too large a value to ignore. Other sources of leaks include the work of part-time specialists who come and go as needed. There are dozens of these specialists, and their combined effort can top 45% of total software effort on large projects.

The book helps software project managers and developers uncover errors in measurements so they can develop meaningful benchmarks to estimate software development efforts. It examines variations in a number of areas that include:

  • Programming languages
  • Development methodology
  • Software reuse
  • Functional and nonfunctional requirements
  • Industry type
  • Team size and experience

Filled with tables and charts, this book is a starting point for making measurements that reflect current software development practices and realities to arrive at meaningful benchmarks to guide successful software projects.

Preface vii
Acknowledgments xi
About the Author xiii
1 Introduction
1(16)
2 Variations in Software Activities by Type of Software
17(12)
3 Variations in Software Development Activities by Type of Software
29(6)
4 Variations in Occupation Groups, Staff Size, Team Experience
35(10)
5 Variations due to Inaccurate Software Metrics That Distort Reality
45(6)
6 Variations in Measuring Agile and CMMI Development
51(8)
7 Variations among 60 Development Methodologies
59(4)
8 Variations in Software Programming Languages
63(6)
9 Variations in Software Reuse from 0% to 90%
69(8)
10 Variations due to Project, Phase, and Activity Measurements
77(6)
11 Variations in Burden Rates or Overhead Costs
83(4)
12 Variations in Costs by Industry
87(6)
13 Variations in Costs by Occupation Group
93(4)
14 Variations in Work Habits and Unpaid Overtime
97(8)
15 Variations in Functional and Nonfunctional Requirements
105(10)
16 Variations in Software Quality Results
115(32)
Missing Software Defect Data
116(1)
Software Defect Removal Efficiency
117(2)
Money Spent on Software Bug Removal
119(2)
Wasted Time by Software Engineers due to Poor Quality
121(1)
Bad Fixes or New Bugs in Bug Repairs
121(1)
Bad-Test Cases (An Invisible Problem)
122(1)
Error-Prone Modules with High Numbers of Bugs
122(1)
Limited Scopes of Software Quality Companies
123(11)
Lack of Empirical Data for ISO Quality Standards
134(1)
Poor Test Case Design
135(1)
Best Software Quality Metrics
135(1)
Worst Software Quality Metrics
136(1)
Why Cost per Defect Distorts Reality
137(2)
Case A Poor Quality
137(1)
Case B Good Quality
137(1)
Case C Zero Defects
137(2)
Be Cautious of Technical Debt
139(1)
The SEI CMMI Helps Defense Software Quality
139(1)
Software Cost Drivers and Poor Quality
139(1)
Software Quality by Application Size
140(7)
17 Variations in Pattern-Based Early Sizing
147(10)
18 Gaps and Errors in When Projects Start. When Do They End?
157(8)
19 Gaps and Errors in Measuring Software Quality
165(56)
Measuring the Cost of Quality
179(42)
20 Gaps and Errors due to Multiple Metrics without Conversion Rules
221(6)
21 Gaps and Errors in Tools, Methodologies, Languages
227(6)
Appendix 1 Alphabetical Discussion of Metrics and Measures 233(100)
Appendix 2 Twenty-Five Software Engineering Targets from 2016 through 2021 333(10)
Suggested Readings on Software Measures and Metric Issues 343(6)
Summary and Conclusions on Measures and Metrics 349(2)
Index 351
Capers Jones