It requires you to develop an information system
model for a system of your own choice. The aim is to get you to apply many of the system analysis and
modelling techniques discussed and illustrated in lectures and tutorials to a real, or at least realistic, case of
non-trivial but still reasonable and do-able complexity.
Choosing your system
As stated in the Introduction, the choice of which system to analyse and model is up to you. However, here
are some rough guidelines to assist you in your choice:
1 Try to choose a system for an area or application about which you know a considerable amount or
about which you are in a position to find out what you need to know. Examples might be a system
related to your current work (if you are presently employed) or previous work experience, a hobby or
interest that you have, or perhaps a system that you or a colleague or friend needs. My experience
in running earlier versions of this course shows unambiguously that this assignment will work best
(and you will learn the most from it) if you choose to analyse and develop a model for a system that
relates to the “real” world in some way, rather than one that is completely fictional and invented
entirely “within your own head”.
2 Try to choose a system of a reasonable size and level of complexity. This will probably be hard to
judge initially, but it is generally better to choose a system that is more likely to turn out to be too big
or complex rather than one that may turn out to be too small or simple. The reason for this is that, if
your choice does turn out to be too big or complex, you can always reduce the scope of your
intended system or choose to model only part of it. On the other hand, if your choice turns out to be
too simple and small to form a useful assignment exercise, it is generally harder to expand it to make
it more suitable as a worthwhile learning experience as well as the basis for an acceptable
assignment submission. Although we won’t discuss these metrics until mid-way through the course
(so they will be less useful to you at the beginning), one useful indicator of size and complexity of a
project is the number of entities in its data model. If this turns out to be somewhere around 6-8 for
your project then you probably have a suitably sized system for this assignment, although these
numbers should definitely not be treated as hard limits. However, if the number of entities in your
data model is significantly fewer than 6 then the model is probably too small and simple to be a
useful learning exercise; and if it is many more than 8 then it is probably starting to get too large to
feasibly tackle. Another indicator is the number of levels you find yourself going down to in your DFD
hierarchy. If this is more than two for the first few processes you decompose, and there are more
than about seven processes on your level 0 diagram, then your chosen system is highly likely to be
too big and you may have to reduce its scope or simply leave parts of the model incomplete…………………………….
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