1. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Non homogeneous and Compound Poisson
Software Reliability Models
ASSE 2010 - 11th Argentine Symposium on Software
Engineering
Néstor R. Barraza
School of Engineering, University of Buenos Aires
2. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Development Phases
Quality:
Standards and Methods
ISO-IEC 9126 (1991)
ISO-IEC 14598 (1995)
ISO-IEC 15504 (1998)
CMMI (1991)
Software Analysis
Reliability
Metrics
3. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Development Phases. Reliability
SR Models
Growth Models
Markov Chains [7]
Clusters [21]
...
4. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
5. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
6. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability Growth Models
Probability of failures is a stochastic process P(N(t) = n).
Number of failures are predicted as the expected value
µ(t) = E[N(t)].
7. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability Growth Models
Model µ(t)
Delayed S-shaped a(1 − (1 + b t)e−b t )
Log Power α lnβ (1 + t)
t
Gompertz a(bc )
(−d t) )
Yamada Exponential a(1 − e−b c (1−e )
8. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Non-homogeneous Poisson process models
λ(t)n
P(N(t) = n) = exp(−λ(t))
n!
λ(t) = a(1 − exp(−bt)) Goel − Okumoto
1
λ(t) = ln(λ0 θt + 1) Musa − Okumoto
θ
E[N(t)] = λ(t)
9. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Compound Poisson process models
m
(λ t)k
P(N(t) = n) = exp(−λt) f ∗k (X1 + X2 + · · · + Xk = n)
k!
k =1
E[N(t)] = λ t E[X ]
10. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Compound Poisson. Compounding Distribution
P(X ) = (1 − r )r x−1 Geometric. Sahinoglu’s first proposal.
aX exp(−a)
P(X ) = X ! 1+exp(a) Poisson Truncated at Zero. This proposal.
11. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Compound Poisson. Parameters Estimation
Mean value unbiased estimator
ˆ m ˆ n
λ= E[X ] =
∆t m
ˆ ˆ
E[N(t)] = λ t E[X ]
n Simple failure rate
= t
∆t
12. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Poisson Truncated at Zero. Parameter Estimation
m
ˆ 1
a= m xi Plackett
i=1
xi >1
n{n−1}
ˆ
a= m
n Tate and Goen (Unbiased Minimum Varianze)
{m}
n
where m is the Stirling number of the second kind
13. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Poisson Truncated at Zero. Mode Estimator
Diminution in the cluster
size as the testing phase
progresses is better
taken into account by the
mode estimator. Also it
has faster adaptation.
14. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability models prediction
15. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability models prediction
16. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability Growth Models Comparison
Compound
Non-Homogeneous
Linear m(t)
Non linear m(t)
Available for several
ML convergence problem
Estimation methods
Input: Mean time
Input: Arrival rate and
between failures
failures clusters size
m(t) adjusted by
m(t) adjusted by the
inhomogenity
cluster size expectation
Parameters are
Parameters are
estimated simultaneously
estimated independently
17. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability Growth Models Comparison
Compound
Non-Homogeneous
Linear m(t)
Non linear m(t)
Available for several
ML convergence problem
Estimation methods
Input: Mean time
Input: Arrival rate and
between failures
failures clusters size
m(t) adjusted by
m(t) adjusted by the
inhomogenity
cluster size expectation
Parameters are
Parameters are
estimated simultaneously
estimated independently
18. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability Growth Models Comparison
Compound
Non-Homogeneous
Linear m(t)
Non linear m(t)
Available for several
ML convergence problem
Estimation methods
Input: Mean time
Input: Arrival rate and
between failures
failures clusters size
m(t) adjusted by
m(t) adjusted by the
inhomogenity
cluster size expectation
Parameters are
Parameters are
estimated simultaneously
estimated independently
19. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability Growth Models Comparison
Compound
Non-Homogeneous
Linear m(t)
Non linear m(t)
Available for several
ML convergence problem
Estimation methods
Input: Mean time
Input: Arrival rate and
between failures
failures clusters size
m(t) adjusted by
m(t) adjusted by the
inhomogenity
cluster size expectation
Parameters are
Parameters are
estimated simultaneously
estimated independently
20. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability Growth Models Comparison
Compound
Non-Homogeneous
Linear m(t)
Non linear m(t)
Available for several
ML convergence problem
Estimation methods
Input: Mean time
Input: Arrival rate and
between failures
failures clusters size
m(t) adjusted by
m(t) adjusted by the
inhomogenity
cluster size expectation
Parameters are
Parameters are
estimated simultaneously
estimated independently
21. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability Growth Models Comparison
Compound
Non-Homogeneous
Linear m(t)
Non linear m(t)
Available for several
ML convergence problem
Estimation methods
Input: Mean time
Input: Arrival rate and
between failures
failures clusters size
m(t) adjusted by
m(t) adjusted by the
inhomogenity
cluster size expectation
Parameters are
Parameters are
estimated simultaneously
estimated independently
22. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability Growth Models Comparison
Compound
Non-Homogeneous
Linear m(t)
Non linear m(t)
Available for several
ML convergence problem
Estimation methods
Input: Mean time
Input: Arrival rate and
between failures
failures clusters size
m(t) adjusted by
m(t) adjusted by the
inhomogenity
cluster size expectation
Parameters are
Parameters are
estimated simultaneously
estimated independently
23. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability Growth Models Comparison
Compound
Non-Homogeneous
Linear m(t)
Non linear m(t)
Available for several
ML convergence problem
Estimation methods
Input: Mean time
Input: Arrival rate and
between failures
failures clusters size
m(t) adjusted by
m(t) adjusted by the
inhomogenity
cluster size expectation
Parameters are
Parameters are
estimated simultaneously
estimated independently
24. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability Growth Models Comparison
Compound
Non-Homogeneous
Linear m(t)
Non linear m(t)
Available for several
ML convergence problem
Estimation methods
Input: Mean time
Input: Arrival rate and
between failures
failures clusters size
m(t) adjusted by
m(t) adjusted by the
inhomogenity
cluster size expectation
Parameters are
Parameters are
estimated simultaneously
estimated independently
25. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability Growth Models Comparison
Compound
Non-Homogeneous
Linear m(t)
Non linear m(t)
Available for several
ML convergence problem
Estimation methods
Input: Mean time
Input: Arrival rate and
between failures
failures clusters size
m(t) adjusted by
m(t) adjusted by the
inhomogenity
cluster size expectation
Parameters are
Parameters are
estimated simultaneously
estimated independently
26. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Compound Non Homogeneous Poisson?
m
(λ(t))k
P(N(t) = n) = exp(−λ(t)) f ∗k (X1 +X2 +· · ·+Xk = n)
k!
k =1
ˆ ˆ
E[N(t)] = λ(t) E[X ]
27. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability and Quality Models
SEI CMM Level Multiplier
Level 1 1.5
Level 2 1
Level 3 0.4
Level 4 0.1
Level 5 0.05
Parameter adjustement as
proposed in [11].
28. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability Actual Practice
Predicted by experts (too conservative or too optimistic)
Lack of failures reports
Failures history ignored
Released time badly estimated
Released when major bugs have been fixed
29. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability Actual Practice
Predicted by experts (too conservative or too optimistic)
Lack of failures reports
Failures history ignored
Released time badly estimated
Released when major bugs have been fixed
30. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability Actual Practice
Predicted by experts (too conservative or too optimistic)
Lack of failures reports
Failures history ignored
Released time badly estimated
Released when major bugs have been fixed
31. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability Actual Practice
Predicted by experts (too conservative or too optimistic)
Lack of failures reports
Failures history ignored
Released time badly estimated
Released when major bugs have been fixed
32. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Software Reliability Actual Practice
Predicted by experts (too conservative or too optimistic)
Lack of failures reports
Failures history ignored
Released time badly estimated
Released when major bugs have been fixed
33. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Conclusions
A comparison between two Poisson based models has
been shown
The behavior of the mode estimator has been studied
Results for real data were analyzed
Advantages and Disadvantages has been pointed out
34. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Bibliography
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35. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
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36. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
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37. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Jeske D. R. and Pham H.: On the Maximum Likelihood
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38. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
Musa, J. D., Okumoto K.: Logarithmic Poisson Execution
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Sahnioglu M. and Can U.: , Alternative Parameter
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39. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
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40. Title
Software Reliability
Software Reliability Growth Models
Non-homogeneous Poisson process models
SRGM Comparison
Software Reliability Actual Practice
Conclusions
Bibliography
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