Software Reliability

Software Reliability

Nikta

Naseri

Javier
Vazquez

Software Reliability

•
Software reliability is the probability of failure
-
free operation of a computer program for a
specified time in a specified environment [1,
p. 15].


Errors to Failures

Software Reliability

•
A research study has shown that professional
programmers average six software defects for
every 1000 lines of code written

[2, p. 476
]
.

Errors in the Code

Degrees of Severity

•
Minor

•
Major

•
Critical

•
Catastrophic

Degrees of Severity

•
Minor

•
Major

•
Critical

•
Catastrophic

Degrees of Severity

•
Minor

•
Major

•
Critical

•
Catastrophic

History

•
“Software Crisis” was
introduced in 1968 at
Garmisch
, Germany.


•
Jelinski

and
Moranda

model appears in 1972.

M
odels

•
A model is a statistical representation that
predicts the reliability of a software.


•
The reliability function:

M
odels

•
A good software reliability model has several
important characteristics. It

–
Gives good predictions of future failure behavior

–
Computes useful quantities

–
Is simple

–
Is widely applicable

–
Is based on sound assumptions [2, pp. 19, 20
]

M
odels

•
The majority of the models capture their data
in the testing phase.

Software Reliability Models

•
SRGM

Software Reliability Growth
Model

•
NHPP

Non
-
homogeneous Poisson
Process Models

Software Reliability Growth Models

•
There are several
models:

–
Coutinho

model

–
De
-
Eutrophication model

–
Jelinski
-
Moranda

model

–
Schick
-
Wolverton

model

–
Wall and Ferguson model

–
Littlewood

Bug
Counting model

Steps for use SRGM

Steps for use SRGM

Steps for use SRGM

Steps for use SRGM

Steps for use SRGM

Steps for use SRGM

Steps for use SRGM

Steps for use SRGM

Disadvantage of SRGMs

•
The main disadvantage of these conventional
SRGMs is the realistic presence of imperfect
debugging.

•
A random process whose probability
distribution varies with the time is called non
-
homogeneous [1, p. 11].

Non
-
homogeneous Poisson
Process model

•
Goel
-
Okumoto

(G
-
O
)

•
P
-
N
-
Z model

•
Delayed
S
-
shaped SRGM

•
Inflection S
-
shaped SRGM

•
HD/G
-
O model

•
Yamamda

exponential

•
Yamada
exponendial

•
Yamada Rayleigh

•
Yamada imperfect debugging model

•
Yamadaimperfect

debugging model(2)

•
New model P
-
Z

•
P
-
Z
model

Non
-
homogeneous Poisson
Process model

NHPP
Goel
-
Okumoto

(G
-
O)

•
Also called exponential mode

•
Concave shape

m(t)

Expected number of errors detected by time
t (“mean value function”)

a(t)

Error content function
i.e.,

total number of errors in the software including
the initial and introduced errors at time
t

b(t)

Error detection rate per error at time
t
[3, p. 180]

NHPP P
-
N
-
Z

•
Assumes introduction rate is a linear function
of testing time and the fault detection rate is
non
-
decreasing with an inflexion S
-
shaped
model

m(t)

Expected number of errors detected by time
t (“mean value function”)

a(t)

Error content function
i.e.,

total number of errors in the software including
the initial and introduced errors at time
t

b(t)

Error detection rate per error at time
t
[3, p. 180]

Model Selection

•
The best way to select between the NHPP
models is getting the values of SSE, MSE, AIC
and RRR. From these values we pick the
smaller, the smaller one it is that better fits
.

Example

Reliability Metrics

Relevance, Experience,
Correctness, Practicality &
Feasibility

•
Analytic Hierarchy Process (AHP) method

–
Software reliability metrics selecting method based on AHP

Reliability Metrics

•
Analytic Hierarchy Process (AHP) method

–
Software reliability metrics selecting method based on AHP

Reliability Metrics

•
Analytic Hierarchy Process (AHP) method

–
Software reliability metrics selecting method based on AHP

Reliability Metrics

•
Analytic Hierarchy Process (AHP) method

–
Software reliability metrics selecting method based on AHP

Reliability Metrics

•
Analytic Hierarchy Process (AHP) method

–
Software reliability metrics selecting method based on AHP

Reliability Metrics

•
Analytic Hierarchy Process (AHP) method

–
Software reliability metrics selecting method based on AHP

Prediction

1.
Benchmarking

•
Previous work



•
Model based on software process (MBPS)




Prediction

•
Prediction model

Prediction

2. General Regression Neural Network (GRNN)

•
Software Reliability prediction based on GRNN

Step1: Collect test data

Step2: Probability distribution functions should determine
for failure time data and test coverage data

Step3:Extend the data

Step4: Establish GRNN

Step5: Train GRNN

Step6: Make prediction based on trained GRNN

Project Management

Useful info

•
A survey conducted in the late
1990’s
by the
American Society for Quality reported that
only 4% of the participants responded
positively when asked if they could use a
software reliability model

[3, p. 5]
,
[4, p. 73]
.


Pros & Cons

•
Advantages

–
Supports decision
making

–
Minimize negative
effects over the project

–
Quantitative results

–
Measurement

–
Improving


•
Disadvantage

–
Complexity


Conclusion

•
T
he
software reliability is good for the
organizations and to their clients and/or
users.

References

•
[
1]

A. I. K. O. John D.
Mulsa
, Software
Reliability, McGraw Hill, 1990.

•
[2]

H. Pham, "Software reliability and cost
models: Perspectives, comparison, and
practice," vol. 149, no. 3, 2003.

•
[3]

H. Pham, System software reliability,
London: Springer, 2006.

•
[4]

X. Li, "Software reliability measurement : a
survey," 2002
.