Software Quality Management Anna University Syllabus B.E. IV CSE About Quality Management Models

1. Software Quality Management
Unit – 4
y y
G Roy Antony Arnold
Asst. Prof./CSE

2. • It is important to
or
when development work is complete.
p p
• It is more important
when it is under
development.
• For these activities, the S f
h i ii h Software
are needed.

4. • On the one h d quality management
h hand, li
models
or so that
.
• On the other hand, they can be
and
.
• They
.

5. • Th reliability growth models, which are
The li bilit th d l hi h
,
therefore,
as for reliability
y
assessment.
• The reliability growth models are useful for
quality management in terms of
for a specific predetermined quality
goal .

7. • Iceberg analogy describes
the Testing Defect Rate
Field
Field
. Defect
Rate
• The
and
.
• The size of the iceberg is
Total Error Injected
in the Software
.

8. • By the time , the
and
.
• The
. To reduce the submerged part,
of the iceberg above the water.

12. • P h
Perhaps the most important principle in software
h i i i l i f
engineering is " .“
• O i t
Our interpretation of the principle, in the context
t ti f th i i l i th t t
of software quality management, is threefold:
– The best scenario is
The best scenario is
.
– When errors are introduced,
,
.
– the phase of
h h f

13. • The Rayleigh model is a
.
• Based on the model, if the error injection rate is
j
reduced,
.
• Also, more defect removal at the front end of the
development process will lead
.
• Myers (1979) states that the
.

14. • Thi
This can serve as the basis for quality
th b i f lit
improvement strategy—especially
1. Plans and actions to reduce error injection
1 Plans and actions to reduce error injection
include
the laboratory‐wide implementation of the
the laboratory‐wide implementation of the
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defect prevention process;
the use of CASE tools for development;
the use of CASE tools for development;
focus on communications among teams to
focus on communications among teams to
f i i
prevent interface defects; and others.
prevent interface defects; and others.

15. 2. To facilitate early defect removal, actions implemented
2 T f ilit t l d f t l ti i l t d
include
• The bidirectional quality improvement strategy is
illustrated in the next Fig. by the Rayleigh model.

19. Greek Biographer and Moralist (AD 46 – 120)
Greek Biographer and Moralist (AD 46

22. User Expectation Software Defect
This software will help me Desired software
accomplish a task.
li h k functionality is missing.
f i li i i i
Clicking on the button Clicking on the button does
performs the task i want to nothing or not what i want it
f th t k tt thi t h ti t it
do. to do.
A file can be successfully
A file can be successfully The file becomes corrupted
The file becomes corrupted
copied to another location. during the copy process.
Calling a method in the API The API fails due to an
Calling a method in the API The API fails due to an
will perform as documented undocumented change to
g y
the registry.

23. • It is theory that decides what can be observed
– Albert Einstein
Albert Einstein
• He who loves practice without theory is like the sailor
who boards ship without a rudder and compass and
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never knows where he may cast.
– Leonardo da Vinci
• E
Experience will answer a question, and a question
i ill i d i
comes from theory. – W Edwards Deming (Father of
Process Improvement).
• A framework, like a theory, provides a means
to ask questions.
• A process framework provides the skeleton of a theory
that can be filled in by the user of the framework.

25. • Th k i th t th h
The key is that the phase‐based defect
b dd f t
removal targets are set to reflect an earlier
defect removal pattern compared to the
defect removal pattern compared to the
baseline.
• Then action plans should be implemented to
Then action plans should be implemented to
achieve the targets.
• As can be seen from the curves, the shifting
As can be seen from the curves, the shifting
of the defect removal patterns does reflect
improvement in the two directions of
(1) earlier peaking of the defect curves, and
( )
(2) lower overall defect rates.

26. • Problem is in assumption of the error injection rate: When
setting d f
i defect removal targets f a project, error i j i
l for j injection
rates can be estimated based on previous experience.
• However, there is no way to determine how accurate such
estimates are when applied to the current release.
• When tracking the defect removal rates against the model,
lower actual d f
l l defect removal could b the result of l
l ld be h l f lower
error injection or poor reviews and inspections.
• In contrast, higher actual defect removal could be the
result of higher error injection or better reviews and
inspections.
• H
How d we k
do know which scenario (b
hi h i (better d f
defect removal,l
higher error injection, lower error injection, or poorer
defect removal) fits the project?
) p j

27. • To solve this problem, an additional indicator,
, is incorporated into the context of the
model for better interpretation of the data.