Information Technology Reference
In-Depth Information
MaxB
(
X
)=
max
p
i
(1)
p
i
−→
RM
i
∈S
RM
RRisk
(
X
)=1
−
p
i
(2)
p
i
−→
RM
i
∈S
RM
where
S
is set of potential evolutions in which X is useful.
=
RM
p
i
r
c
st.
useful
(
X, RM
ij
)
∗
→
S
−→
RM
i
|∃
(
RM
i
RM
ij
)
∈
One may argue about the rationale of these two metrics. Because he (or she)
can intuitively measure the usefulness of an element set by calculating the
Total
Belief
which is exactly the complement of our proposed
Residual Risk
. However,
using only Total Belief (or
Residual Risk
) may mislead designers in case of a
long-tail
problem.
The long-tail problem, firstly coined by Anderson [1], describes a larger pop-
ulation rests within the tail of a normal distribution than observed. A long-tail
example depicted in Fig. 2 where a requirement model RM might evolve to
several potential evolutions with very low probabilities (say, eleven potential
evolutions with 5% each), and another extra potential evolution with dominat-
ing probability (say, the twelfth one with 45%). Suppose that an element A
appears in the first eleven potential evolutions, and an element B appears in the
last twelfth potential evolution. Apparently, A is better than B due to A's total
belief is 55% which is greater than that of B, say 45%. However, at the end of
the day, only one potential evolution becomes effective (
i.e.
chosen by Reality)
rather than 'several' potential evolutions are together chosen. If we thus consider
every single potential evolution to be chosen, the twelfth one (45%) seems to be
the most promising and
Max Belief
makes sense here. Arguing that A is better
than B or versa is still highly debatable. Ones might put their support on the
long tail [1], and ones do the other way round [5]. Therefore, we introduce both
Residual Risk
and
Max Belief
to avoid any misleading in the decision making
process that can be caused when using only Total Belief.
For a better understanding of
Max Belief
and
Residual Risk
, we conclude this
section by applying our proposed metrics to the evolution of SWIM
Security Services discussed in previous section. In Fig. 3, here we have an initial re-
quirement model RM0(ISS-ENT-1,ISS-BP-1) that will evolve to RM1(ISS-ENT-
2,ISS-BP-1), RM2(ISS-ENT-1,ISS-BP-2), and RM3(ISS-ENT-2,ISS-BP-2) with
probabilities of 28%, 18% and 42%, respectively. There are 12% that RM0 stays
RM
A
A
A
B
RM
1
RM
2
…....
RM
11
RM
12
Fig. 2.
The long-tail problem
