190 Software teSting interview QueStionS
(a) hoW do you estimate White Box testing?
The testing estimates derived from function points are actually the estimates
for white box testing. So in the following figure the man days are actually the
estimates for white box testing of the project. It does not take into account
black box testing estimation.
(a) is there a Way to estimate acceptance test cases
in a system?
Total acceptance test cases 5 total adjusted function points multiplied by 1.2:
The total estimate for this project is 37.7 man days.
FIGURE 169 Total estimation of the accounting application
Now that we have completed the function point analysis for this project let’s
move on to the second step needed to calculate black box testing using TPA.
teSting eStimation 191
Step 2: Calculate Df (Function-dependant factors)
Df is defined for each function point. So all the function point descriptions
as well as values are taken and the Dfs are calculated for, each of them. You
can see from the figure how every function point factor is taken, and how
the Df is calculated.
FIGURE 170 Df calculated
But we have still not seen how Df will be calculated. Df is calculated using
four inputs: user importance, usage intensity, interfacing, and complexity.
The following figure shows the different inputs in a pictorial manner. All four
factors are rated as low, normal, and high and assigned to each function are
factors derived from the function points. Let’s take a look at these factors.
FIGURE 171 Factors on which Dfs depend
192 Software teSting interview QueStionS
User importance (Ue): How important is this function factor to the user
compared to other function factors? The following figure shows how they are
rated. Voucher data, print voucher, and add voucher are rated with high user
importance. Without these the user cannot work at all. Reports have been
rated low because they do not really stop the user from working. The chart
of accounts master is rated low because the master data is something which
is added at one time and can also be added from the back end.
FIGURE 172 User importance
Usage intensity (Uy): This factor tells how many users use the application
and how often. The following figure shows how we have assigned the values
to each function factor. Add voucher, Print Voucher, and voucher data are the
most used function factors. So they are rated high. All other function factors
are rated as low.
Figure 173 Usage intensity
teSting eStimation 193
Interfacing (I): This factor defines how much impact this function
factor has on other parts of the system. But how do we now find the impact?
In TPA, the concept of LDS is used to determine the interfacing rating.
LDS stands for Logical Data Source. In our project we have two logical data
sources: one is voucher data and the other is account code data (i.e., chart
of accounts data). The following are the important points to be noted which
determine the interfacing:
We need to consider only functions which modify LDS. If
a function is not modifying LDS then its rating is Low by
default.
To define LDS we need to define how many LDSs are
affected by the function and how many other functions access
the LDS. Other functions only need to access the function;
even if they do not modify it.
The following is the table which defines the complexity level according
to the number of LDSs and functions impacting on LDS.
n
n
FIGURE 174 LDS and the function concept
194 Software teSting interview QueStionS
So now depending on the two points defined above let’s try to find out the
interfacing value for our accounting project. As said previously we have two
functions which modify LDS in our project: one is the add voucher function
which affects the voucher data and the add account code which affects the
chart of accounts code (i.e., the accounts code master). The add voucher
function primarily affects voucher data LDFs. But other functions such as
reports and print also use the LDS. So in total there are five functions and
one LDS. Now looking at the number of LDSs and the number of functions
the impact complexity factor is Low.
FIGURE 175 LDS ratings
FIGURE 176 Add voucher data
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