1. Chapter 15 Projecting Defects( 缺陷预测 )

2. 山东大学齐鲁软件学院 2 outline  Analyze and use your defect data to help improve both planning accuracy and product quality.  Determine how best to prevent or find the defects you inject.  Control the quality of your work.

3. 山东大学齐鲁软件学院 3 Defect rates  Defect injection rate typically range from about 50~250 defects per KLOC.  With Figure15.1 & 15.2 shown:  Defect density as a function of the years.  Some quality practices learn with experience.  defect levels are largely a matter of personal discipline.  After PSP training, engineers understood the numbers and types of defects. And then reduced their total defect injection rates.

4. 山东大学齐鲁软件学院 4 Defect rates number are big!  Defect injection is a natural consequence of being human.  Average 100 defects/KLOC  About 50 defects/KLOC after PSP training.  So there are 50,000 defects in a 1,000,000 LOC program.  All of them must be found and fixed before delivery.

5. 山东大学齐鲁软件学院 5 Using defect data  Gather defects data---help understanding the defects you inject to your program.  Design a personal checklist---for code reviewing to remove defects.  Establish a code standard---help to prevent defects injected in coding.  Then what’s our next step? what’s our final goal?

6. 山东大学齐鲁软件学院 6 Using Defect Data  Task: Using historical defect data to make a reasonably good projections of the number of defects you will inject and remove in each phase of a programming project.  Advantage: By accurate estimates of defect levels, you can determine the likely quality of the program being developed. Then you can decide whether additional defect-removal steps are needed.  The final goal---defect-free programming, can not be achieved only by estimate, it should be approached by systematic work and step by step.  Defects collect,  Defect remove and prevent---code review and code standard,  Defect estimate. Someday you will likely work on a project where defect-free work is essential!

7. 山东大学齐鲁软件学院 7 Defect density  Defect density(Dd): the defects per thousand lines of code, measured by defects/KLOC.  Add up the total numbers of defects found.  Count the number of new and changed LOC  Calculate the defects per KLOC  Be sure not include library routine and copy part in counting LOC.

8. 山东大学齐鲁软件学院 8 The calculation of Dd  Add up the total number of defects(D) found in all phases of the process,  Count the number(N) of new and changed LOC in the program,  Calculate the defects per KLOC as Dd = 1000*D/N.  For example, a 96 LOC program had a total 14 defects, then Dd = 1000*14/96 = 145.83 defects/KLOC.

9. 山东大学齐鲁软件学院 9 Projecting Defects  Projecting defects  and then finally we achieve that we have ability to write a defect-free program  Then how to project defects in your program?  Plan Time  Plan project schedule  Then can defects be planned?

10. 山东大学齐鲁软件学院 10 Difficulties of Projecting Defects Rates  Without experiences  Not be sure how some function or procedures works  Confused by language construct  Encounter new compiler or new programming enviroments  Solution:  Make more practices, to improve your skills and enrich your experience of programming  Gather and analyze defects data to continue improving.

11. 山东大学齐鲁软件学院 11 Difficulties of Projecting Defects Rates  The process is not stable  The process is a new method and procedure to you,  defects injected, defects fix time are all fluctuating, especially defects fix may inject defect again.  Solution:  Improve the process by spending sufficient time on code reviewing to settle down the process.

12. 山东大学齐鲁软件学院 12 Defect estimation  Using data of previously developed programs to calculate the D plan  Dd plan = 1000*(D 1 +D 2 +…+D i )/(N 1 +N 2 +…+N i )  D plan = N plan *Dd plan /1000 ，  or D plan = N plan *D to date /N to date.  The calculation of expected number of defects to be injected and removed in each phase.  D phase = D planned total *D to date % /100.

13. 山东大学齐鲁软件学院 13 The updated project plan summary form and example  By using the project plan summary of a most previously developed program, make time plan and defect plan first.  Then by actual data, to complete the project plan summary for the new developed program.  Calculate the plan data  Calculate the To Date data  Entering the actual data

14. 山东大学齐鲁软件学院 14 project plan summary

15. 山东大学齐鲁软件学院 15 project plan summary

16. 山东大学齐鲁软件学院 16 project plan summary

17. 山东大学齐鲁软件学院 17 summary  Defect density.  Estimate defects injected.

18. 山东大学齐鲁软件学院 18 Assignment  For the next program you write:  Determine the defects/KLOC rate and estimate the defects to be injected and removed by phase.  Inter these data in project plan summary.

19. Chapter 16 The Economics of Defect Removal

20. 山东大学齐鲁软件学院 20 outline  It’s trade-offs ( 平衡 )between cost, schedule and quality;  It concerns three aspects:  The relative costs of removing defects during development;  The impact of any remaining defects on the customer;  The resulting customer support costs.  Hence, it’s a trade-off of risk and benefit.

21. 山东大学齐鲁软件学院 21 The need for quality work  Defect-free software: no extra costs for defect removal, no risk for potential costs. But it’s always a final goal since the beginning of software development.  Defect removal costs a lot, especially in later phase.  Defect removal lowers benefit explicitly sometimes.  Defect removal does not bring benefit, but it can lower risk implied by remaining defects.

22. 山东大学齐鲁软件学院 22 Defect-Removal problem  Defect-Removal is a software size and complexity related work.  The size and complexity of software system has increased ten times every ten years.  Laser printer driver: 20, 000 LOC - --1 million LOC;  More and more application, more and more complex;

23. 山东大学齐鲁软件学院 23 Measurement of defect-removal  Measure defect-removal effectiveness  Defect-Removal Rate: number of defects removed in one hour.  Defect-Removal Yield( 效益 ): Percentage of the defects found by a removal method.

24. 山东大学齐鲁软件学院 24 Defect-Removal Time  Defect types  Requirement defect;  Design defect;  Coding injected defects  Defect-removal moment  Code reviewing;  Compiling;  Testing: Unit test and integrated test.  Varying from several minutes to several days  250 engineers spent a full year on Microsoft NT system test finding and fixing 30,000 defects. This averages 16 hours per defect.

25. 山东大学齐鲁软件学院 25 Defect injection and defect removal experience  The factors which affect the cost of defect- removal  Defect injection;  Defect removal experience  Figure 16.1 shows that bigger defect injection rate is made in coding than in design.  Figure 16.2 demonstrates that defect- removal in testing, code reviewing and compile, and in the phase of code reviewing, one can find more defects than in testing phase.

26. 山东大学齐鲁软件学院 26 Defect Removal Savings  Compare the results of defect- removal with or without PSP. See table 16.1 in Page 214.  With PSP, software engineers have a great time savings.  With PSP, software product has less remaining defects (risks).

27. 山东大学齐鲁软件学院 27 Defects/Hour on the PSP project plan summary  Comparison of defect injection/ removal rates of different phase  Def./Hour = 60*(To date defects injected/removed in one phase)/(To date Time in one phase (Min)).  See example in table 16.2 in page 216

28. 山东大学齐鲁软件学院 28 Yield of defect removing  Def. Removed/Hour: Defects found rate in one phase  Also we would like to know how many defects were found and how many were missed in one defects removing phase--- Yield  Moreover,  Process Yield = the percentage of the defects found before the first compile and test.  That is the percentage of the defects found in review phases (code review and design review), the percentage of the defects found initiatively （ 主动地）, not passively （被动地）.

29. 山东大学齐鲁软件学院 29 Yield Calculation  Yield plan  Yield actual  Yield To Date  Yield = 100*(Defects removed before compile)/(Defects injected before compile).  See example of calculation in table 16.2

30. 山东大学齐鲁软件学院 30 Improving Defect- Removal Rates  Focus on yield first. First objective must be to consistently achieve yields of 70% or more.  Do the code review before the first compile, and use the compiler to check the quality of your coding review.  Prone your code review checklist to improve your defect removal rates.  One definition of insanity is doing the same thing over and over and expecting a different result [Brown].

31. 山东大学齐鲁软件学院 31 Improving Defect- Removal Rates  gathering defects data  Calculating yield and defect-injection and defect-removal rates  Track these data in your programming practices.  One Objective of this doings: Making you conscious of the costs and consequences of defects, and then do the code review initiatively and efficiently.

32. 山东大学齐鲁软件学院 32 Reducing Defect- Injected Rates  Record all your defects.  Be aware of defects, you will work more carefully and will reduce the number of defects of defects you inject.  Produce better design  Design process runs on the high level, more logically than coding, thus one makes less defects within the same time in design than in coding phase.  A complete and better-documented design will prevent defects injection in coding phase.  Frame guided  Save coding time

33. 山东大学齐鲁软件学院 33 Reducing Defect- Injected Rates  Use better methods.  Objected oriented method  UML (Unified modeling language) and use case  Consistent coding convention.  Use better tools  Integrated programming environment  Visual interface of programming.

34. 山东大学齐鲁软件学院 34 Summary  Defects are a principal cause of software problems. The remaining defects may lead severe consequences.  Defect fixing costs money and delays project schedules, and the defect fix times are unpredictable. So defect management is a economic issue.  Therefore, we need capture the key points in defects removing, improve defect removal rates and yield.  On the other hand, we need to reduce the defects injection rate by using better design, methods and tools.

35. 山东大学齐鲁软件学院 35 Two measurements  Defects/Hour  Yield