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Defect Density is the number of confirmed
defects detected in software/component
during a defined period of
development/operation divided by the size
of the software/component.
• It is Manual Testing
• Size (Function Pts, SLOC, Modules, Subsystems, Pages,
Documents )
Defect
• confirmed and agreed upon (not just reported).
• Dropped defects are not counted.
Size
• Function Points (FP)
• Source Lines of Code
• Many metrics have parameters such as “total number of defects”
e.g. total number of requirements defects.
• Clearly, we only ever know about the defects that are found.
• So we never know the “true” value of many of these metrics.
• Further, as we find more defects, this number will increase:
• Hopefully, finding defects is asymptotic over time i.e. we find
fewer defects as time goes along, especially after release.
• So metrics that require “total defects” type info will change over
time, but hopefully converge eventually.
• The later in the lifecycle we compute the metric, the more
meaningful the results.
• If and when we use these metrics, we must be aware of this effect
and account for it.
5
• Many defect metrics have “size” parameters:
• The most common size metric is KLOC (thousands of lines of
code).
• Depends heavily on language, coding style, competence.
• Code generators may produce lots of code, distort
measures.
• Does not take “complexity” of application into account.
• Easy to compute automatically and “reliably” (but can be
manipulated).
• An alternative size metric is “function points” (FP’s).
• A partly-subjective measure of functionality delivered.
• Directly measures functionality of application: number of
inputs and outputs, files manipulated, interfaces provided
etc.
• More valid but less reliable, more effort to gather.
• We use KLOC in our examples, but works just as well with FP’s. 6
•Module 1 = 20 bugs
•Module 2 = 30 bugs
•Module 3 = 50 bugs
•Module 4 = 60 bugs
And the total line of code for each module is
•Module 1 = 1200 LOC
•Module 2 = 3023 LOC
•Module 3 = 5034 LOC
•Module 4 = 6032 LOC
Then, we calculate defect density as
Total bugs = 20+30+50+60 = 160
Size= 15289 LOC
Defect Density = 160/15289 = 0.01046504 defects/loc =
10.465 defects/Kloc
• Size estimation itself has problems.
• “Total Defects” problem.
• Defects may not equal reliability. (User experience
problem).
• Statistical significance when applied to phases and
components.
• Actual number of defects may be so small that
random variation can mask significant variation.
14
• Very useful as measure of organizational capability to
produce defect-free outputs.
• Can be compared with other organizations in the
same domain.
• Outlier information useful to spot problem projects
and problem components.
• Can be used in-process, if comparison is with defect
densities of other projects in same phase.
• If much lower, may indicate defects not being found.
• If much higher, may indicate poor quality of work.
• (In other words, need to go behind the numbers to
find out what is really happening. Metrics can only
provide triggers)
16
• For comparing the relative number of
defects in various software components
so that high-risk components can be
identified and resources focused towards
them.
• For comparing software/products so that
quality of each software/product can be
quantified and resources focused towards
those with low quality.
•Thank
You

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Defect Density matrix SQA

  • 1.
  • 2. Defect Density is the number of confirmed defects detected in software/component during a defined period of development/operation divided by the size of the software/component.
  • 3. • It is Manual Testing • Size (Function Pts, SLOC, Modules, Subsystems, Pages, Documents )
  • 4. Defect • confirmed and agreed upon (not just reported). • Dropped defects are not counted. Size • Function Points (FP) • Source Lines of Code
  • 5. • Many metrics have parameters such as “total number of defects” e.g. total number of requirements defects. • Clearly, we only ever know about the defects that are found. • So we never know the “true” value of many of these metrics. • Further, as we find more defects, this number will increase: • Hopefully, finding defects is asymptotic over time i.e. we find fewer defects as time goes along, especially after release. • So metrics that require “total defects” type info will change over time, but hopefully converge eventually. • The later in the lifecycle we compute the metric, the more meaningful the results. • If and when we use these metrics, we must be aware of this effect and account for it. 5
  • 6. • Many defect metrics have “size” parameters: • The most common size metric is KLOC (thousands of lines of code). • Depends heavily on language, coding style, competence. • Code generators may produce lots of code, distort measures. • Does not take “complexity” of application into account. • Easy to compute automatically and “reliably” (but can be manipulated). • An alternative size metric is “function points” (FP’s). • A partly-subjective measure of functionality delivered. • Directly measures functionality of application: number of inputs and outputs, files manipulated, interfaces provided etc. • More valid but less reliable, more effort to gather. • We use KLOC in our examples, but works just as well with FP’s. 6
  • 7. •Module 1 = 20 bugs •Module 2 = 30 bugs •Module 3 = 50 bugs •Module 4 = 60 bugs And the total line of code for each module is •Module 1 = 1200 LOC •Module 2 = 3023 LOC •Module 3 = 5034 LOC •Module 4 = 6032 LOC Then, we calculate defect density as Total bugs = 20+30+50+60 = 160 Size= 15289 LOC Defect Density = 160/15289 = 0.01046504 defects/loc = 10.465 defects/Kloc
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14. • Size estimation itself has problems. • “Total Defects” problem. • Defects may not equal reliability. (User experience problem). • Statistical significance when applied to phases and components. • Actual number of defects may be so small that random variation can mask significant variation. 14
  • 15.
  • 16. • Very useful as measure of organizational capability to produce defect-free outputs. • Can be compared with other organizations in the same domain. • Outlier information useful to spot problem projects and problem components. • Can be used in-process, if comparison is with defect densities of other projects in same phase. • If much lower, may indicate defects not being found. • If much higher, may indicate poor quality of work. • (In other words, need to go behind the numbers to find out what is really happening. Metrics can only provide triggers) 16
  • 17. • For comparing the relative number of defects in various software components so that high-risk components can be identified and resources focused towards them. • For comparing software/products so that quality of each software/product can be quantified and resources focused towards those with low quality.