Information Technology Reference
In-Depth Information
With regard to the navigational model, a mean of 2.2 UPs (std. dev. 0.83) were de-
tected by the evaluators since the navigation steps for carrying out possible actions are
considered to be proper by values of metrics such as navigation density, navigation
depth, etc. However, UPs related to the lack of return paths were detected (e.g., “does
not provide backs link” or “no link to go home” ). These can be solved in the naviga-
tional model itself by adding links that connect the information previously obtained.
With regard to the presentation model, a mean of 10.6 UPs (std. dev. 1.51) were
detected by the evaluators since the expressiveness of its modeling primitives allows
several metrics to be operationalized (e.g., “error messages are not meaningful” and
“links and labels are not discernible” ). The former can be solved in the presentation
model itself by correcting the label that shows the message, and the latter can be
solved in the navigational model since names for links can be provided as properties
of them.
With regard to the final user interface, a mean of 12.2 UPs (std. dev. 1.78) were de-
tected by the evaluators. Some examples of the UPs detected were related to the sup-
port to operation cancellation (e.g., “ the creation of a new task cannot be canceled” ).
This can be solved in the navigational model since links can be added to permit can-
celation paths. Other UPs related to the immediate feedback that provide UI controls
were also detected (e.g., “tabs of the main menu do not show the current user state” ).
This can be solved in the transformation rules that map the representation of tabs with
a specific UI control of the technological platform.
It is important to note that some usability attributes such as the uniformity of the
UI position are directly supported by the modeling primitives of the APDs.
Lessons Learned. The case study has been useful in that it has allowed us to learn
more about the potentialities and limitations of our proposal and how WUEP can be
improved.
WUEP can detect several usability problems from a wide range of types in several
artifacts employed during the early stages of an MDWD process. The application of
operationalized metrics and traceability among models not only provides a list of
usability problems, but also facilitates the provision of recommendations with which
to correct them. Metric operationalization also allows WUEP to be applied to differ-
ent MDWD processes by establishing a mapping between the generic description of
the metric and the modeling primitives of artifacts, in addition to other traditional
development processes, by operationalizing metrics only in final user interfaces.
However, usability reports will only provide feedback to the implementation stage
since traceability between artifacts is not well-defined. Moreover, the evaluation
process may be a means to discover which usability attributes are directly supported
by the modeling primitives or to discover limitations in the expressiveness of these
artifacts.
During the execution of the case study, several aspects related to how WUEP is
applied were detected to be improved. For example, the application of metrics was
detected as being a very tedious task when performed manually, particularly when the
metrics provided a complex calculation formula (e.g., Compactness, Depth of the
navigation, etc.). In addition, some metrics obtained different values depending on the
evaluator since these metrics imply a certain degree of subjectivity (e.g., Discernible
links, properly chosen Metaphors, etc.). These issues could be alleviated, at least to
some extent, by providing better guidelines in order to minimize the subjectivity in
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