Hardware Reference
In-Depth Information
A Sample Case
.
In order to evaluate this model, we created a sample case
2
and exe-
cuted it to obtain the optimum set of measures for a specified set of goals. Specifi-
cally, 5 goals were defined and 11 questions were identified which would provide the
information required. Each question can be answered by means of a number of meas-
ures associated with it. For this case, we identified 23 suitable measures from the
Measures Pool.
Then, we collected the data for each measure according to the factors defined in
OMSD. Among those measures, we observed that 3 of the measures are to be used for
answering more than one question. Therefore, we had 20 measures for the further
decision making process. After this step, we calculated the effort required to collect
each measure, checked the dependencies of the measures, and at the end calculated
the cumulative cost for each measure by adding any additional expenses if exist. We
performed the same calculations for the all 22 measures.
Next, we created the 'Attribute-measure matrix' and then made the decisions using
the screening rules in the OMSD model. In this matrix, cumulative costs for all of the
measures are obtained in the previous step along with the Importance value decided
by the managers according to the importance of each goal. For example, 'Productiv-
ity' measure had a cumulative cost 350$ and its importance value is 3. These meas-
ures are then mapped to the relevant attributes. For example, Development effort
measure as well as any product size measure is required to derive the measure for
answering the question related to the productivity attribute. Using the screening rules,
we saw that two size measures can be chosen to derive the measure for the productiv-
ity. Therefore, we first considered all three measures (Development Effort, SLOC,
FP). Then, we checked the 'No. of usage' attribute for each measure globally. FP is to
be used more than SLOC, which means that it can be used to satisfy other goals as
well. Here, SLOC and FP are equally important and the cost for measuring FP is
higher. Based on this information, the OMSD model decided to choose FP since it can
be used to answer a couple of questions which reduces the total cost.
After each execution for each measure, there checked the Constraint cost. After se-
lection of each measure, the remaining available budget is re-calculated by deducting
the cost for the selected measure from it. It is important to note that if two measures
have the same 'No. of usage' but different importance and cost, then a tradeoff between
importance and cost is made by the measurement responsible(s) and/or managers.
At the end of the whole process, OMSD model decided on 8 measures from suit-
able 23 measures. This is the optimal set of measures as it helps achieving the goals
under the defined constraints and identified factors.
Although we obtained a smaller measures set in this experiment, our main purpose
in this case study was not to show the model's efficiency but rather to test the appli-
cability and the rules of the model. The model is dependent on the initial measures set
as well as the constraints set by the measurement responsible.
3 Conclusions
Measurement process is one of the critical processes, which leads organizations to-
wards process improvement. Since numbers of measures are available, it is needed to
2
The sample case can be found at: http://sites.google.com/site/omsd09/sample-case
