Page 145 - DCAP405_SOFTWARE_ENGINEERING

Software Engineering

Notes Model the system: Running models clarifies requirements, reveals bottlenecks and
fragmented activities, reduces cost and exposes duplication of efforts.

Integrate: Integration means designing interfaces and bringing system elements together
so they work as a whole. This requires extensive communication and coordination.
Launch the system: Launching the system means running the system and producing
outputs — making the system do what it was intended to do.
Assess performance: Performance is assessed using evaluation criteria, technical performance
measures and measures — measurement is the key. If you cannot measure it, you cannot
control it. If you cannot control it, you cannot improve it.
Re-evaluation: Re-evaluation should be a continual and iterative process with many
parallel loops.
We live in a world of systems driven by cause and affect. Those systems include financial,
production, inventory, biological, chemical, thermodynamic or workflow. Systems can be
modeled as nodes representing system variables and connecting lines representing causal effects.
The changing value of one variable can cause another to increase or decrease as described by
equations. Understanding how a system really works is the first step towards using, improving,
automating or explaining it to others.

This unit explores the use of modelling and simulation as a problem solving tool.
We undertake this discussion within the framework of a modelling and simulation project. This
project framework embraces two key notions; first there is the notion of a ‘system context’; that
is, there is a system that has been identified for investigation, and second, there is a problem
relating to the identified system that needs to be solved. Obtaining an acceptable solution to this
problem is the goal of the modelling and simulation project. We use the term ‘system’ in its
broadest possible sense; it could, for example, include the notions of a process or a phenomenon.
Furthermore, physical existence of the system is not a prerequisite; the system in question may
simply be a concept, idea, or proposal. What is a prerequisite, however, is the requirement that
the system in question exhibit ‘behaviour over time,’ in other words, that it be a dynamic
system.

Systems, or more specifically dynamic systems, are one of the most pervasive notions of our
contemporary world. Broadly speaking, a dynamic system is a collection of interacting entities
that produces some form of behaviour that can be observed over an interval of time. There are,
for example, physical systems such as transportation systems, power generating systems, or
manufacturing systems. On the other hand, in less tangible form, we have healthcare systems,
social systems, and economic systems. Systems are inherently complex and tools such as
modelling and simulation are needed to provide the means for gaining insight into features of
their behaviour. Such insight may simply serve to provide the intellectual satisfaction of deeper
understanding or, on the other hand, may be motivated by a variety of more practical and
specific reasons such as providing a basis for decisions relating to the control, management,
acquisition, or transformation of the system under investigation (the SUI).
The defining feature of the modelling and simulation approach is that it is founded on a computer-
based experimental investigation that utilises an appropriate model for the SUI. The model is a
representation or abstraction of the system. The use of models (in particular, mathematical
models) as a basis for analysis and reasoning is well established in such disciplines as engineering
and science. It is the emergence and widespread availability of computing power that has made
possible the new dimension of experimentation with complex models and hence, the emergence
of the modelling and simulation discipline.

138 LOVELY PROFESSIONAL UNIVERSITY