Information Technology Reference
In-Depth Information
for
n
workload units, the estimated reliability
R
according to the Nelson model
[36]
,
one of the most widely used IDRMs, can be obtained as:
n − f
n
f
n
R =
=
1
−
=
1
− r.
Where
r
is the failure rate, which is also often used to characterize reliability. When
usage time
t
i
is available for each workload unit
i
, the summary reliability measure,
mean-time-between-failures (MTBF), can be calculated as:
1
f
MTBF
=
t
i
.
i
If discovered defects are fixed over the observation period, the defect fixing effect
on reliability (or reliability
growth
due to defect removal) can be analyzed by using
various SRGMs
[27,34]
. For example, in the widely used Goel-Okumoto model
[17]
,
the failure arrival process is assumed to be a non-homogeneous Poisson process
[22]
.
The expected cumulative failures,
m(t )
, over time
t
is given by the formula:
1
−
e
−bt
m(t ) = N
where the model constants
N
(total number of defects in the system) and
b
(model
curvature) need to be estimated from the observation data.
3.
Web Characteristics, Challenges, and Opportunities
Web applications possess various unique characteristics that affect the choices of
appropriate techniques for web testing and reliability improvement. We next examine
these characteristics in relation to existing software testing techniques and address
general questions about web quality and reliability.
3 . 1 C h a r a c t e r i z i n g W e b P r o b l e m s a n d W e b Te s t i n g N e e d s
One of the fundamental differences between web-based applications and tradi-
tional software is the
document and information focus
for the former as compared
to the computational focus for the latter. Although some computational capability
has evolved in newer web applications, document and information search and re-
trieval still remain the dominant usage for most web users. Most of the documents
and information sources are directly visible to the web users, as compared to the
complicated background computation associated with traditional software systems.
Consequently, the line that distinguishes black-box testing from white-box testing is
blurred for the web environment.
