Module 2: Defect Chemistry and Defect Equilibria Introduction ...
Statistical Software Quality Assurance Implies –Information about defects is collected and...
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Transcript of Statistical Software Quality Assurance Implies –Information about defects is collected and...
Statistical Software Quality Assurance
• Implies– Information about defects is collected and
categorized– An attempt is made to trace each defect to
underlying cause– Use of Pareto Principle to identify vital
causes(80% of defects can be traced to 20% of
causes/mistakes)– Move to correct the problems that have
caused the defects
Software Engineering II
Lecture 30
Fakhar Lodhi
Recap
Example• Information about defects is collected for one year and
categorized as follows:
1. Incomplete or erroneous specifications (IES)2. Misinterpretation of customer communication (MCC)3. Intentional deviation from specifications (IDS)4. Violation of programming standards (VPS)5. Error in data representation (EDR)6. Inconsistent component interface (ICI)7. Error in digital logic (EDL)8. Incomplete or erroneous testing (IET)9. Inaccurate or incomplete documentation (IID)10. Error in programming language translation or design (PLT)11. Ambiguous or inconsistent human computer interface
(HCI)12. Miscellaneous (MIS)
Error Category Serious Moderate Minor Sub Total
IES 34 68 103 205
MCC 12 68 76 156
IDS 1 24 23 48
VPS 0 15 10 25
EDR 26 68 36 130
ICI 9 18 31 58
EDL 14 12 19 45
IET 12 35 48 95
IID 2 20 14 36
PLT 15 19 26 60
HCI 3 17 8 28
MIS 0 15 41 56
Total 128 379 435 942
Error Category Sub Total% of Total
errors
IES 205 22
MCC 156 17
IDS 48 5
VPS 25 3
EDR 130 14
ICI 58 6
EDL 45 5
IET 95 10
IID 36 4
PLT 60 6
HCI 28 3
MIS 56 6
Total 942
IES, MCC and EDR are vital errors - cause
53% of all errors
Error Category Serious
% of Serious errors
IES 34 27
MCC 12 9
IDS 1 1
VPS 0 0
EDR 26 20
ICI 9 7
EDL 14 11
IET 12 9
IID 2 2
PLT 15 12
HCI 3 2
MIS 0 0
Total 128
IES, EDR, PLT and EDL constitute about 80% of serious errors
Excel example
• Now start corrective action focused on vital few
• For example for EDR – Review the data representation techniques
to identify the possible improvement areas– Adopt a use case tool for data modeling and
perform stringent data design reviews
Error Index (EI) Used to develop an overall indication of
improvement in software quality
• Ei – the total number of errors uncovered during the ith step in the SE process
• Si – number of serious errors• Mi – number of moderate errors• Ti – number of minor errors• PSi – product size at the ith step• ws, wm, wt – weighting factors for serious,
moderate, and minor errors• Recommended values – 10, 3, 1 respectively
Error Index (EI)• At each step of the software process a Phase
Index is computed PIi = ws(Si/Ei) + wm(Mi/Ei) + wt(Ti/Ei)
• EI – cumulative effect on each PIi = ∑(i x PIi)/PSi
– Weighting errors encountered in the SE processes more heavily than those encountered earlier
– Used to develop an overall indication of improvement in software quality
Software Reliability
• Defined as:– Probability of failure free operation of a
computer program in a specified environment for a specified time
– E.g. program X is estimated to have a reliability of 0.96 over 8 elapsed hours.
• What is meant by the term failure?– Failure is non-conformance to software
requirements– Grading
• From annoying to catastrophic• Time to fix from minutes to months• Ripples from fixing
Software reliability
• Hardware versus software reliability– Hardware reliability is predicted on failure
due to wear, rather than failure due to design
– Software – no wear and tear• Mean time between failure – MTBF
– MTBF = MTTF + MTTRwhere MTTF is mean time to failure andMTTR is mean time to repair
Software reliability
• Arguably MTBF is far better than defects/kloc– Each error does not have the same failure
rate– User is concerned with failure and not with
total error count