Abstract
M.Eng. (Engineering Management).
This dissertation summarizes several classes of software cost estimation models
and techniques. Experience to date indicates that expertise-based techniques
are less mature than the other classes of techniques (algorithmic models), but
that all classes of techniques are challenged by the rapid pace of change in
software technology. The primary conclusion is that no single technique is best
for all situations, and that a careful comparison of the results of several
approaches is most likely to produce realistic estimates.
As more pressure on accurate cost estimation increase, research attention is
now directed at gaining a better understanding of the software-engineering
process as wall as constructing and evaluating software cost estimation tools.
This dissertation evaluated four of the most popular algorithmic models used to
estimate software cost (SLIM, COCOMO II, Function points and SLOC)
This dissertation also provides an overview of the baseline cost estimation model
tailored to these new forms of software engineering. The major new modeling
capabilities are an adaptable family of software sizing models, involving Function
Points and Source Lines of Code. These models are serving as a framework for
an extensive current data collection and analysis effort to further refine and
calibrate the model's estimation capabilities.