new paper, ESE journal, dec 2009

[Tim Menzies]

/wisp/var/adam2/papers/cbr/09adjustment.pdf

Cost estimation is one of the most important but most difficult tasks
in software
project management. Many methods have been proposed for software cost
estimation.
Analogy Based Estimation (ABE), which is essentially a case-based
reasoning (CBR)
approach, is one popular technique. To improve the accuracy of ABE
method, several
studies have been focusing on the adjustments to the original
solutions. However, most
published adjustment mechanisms are based on linear forms and are
restricted to numerical
type of project features. On the other hand, software project datasets
often exhibit non-
normal characteristics with large proportions of categorical features.
To explore the
possibilities for a better adjustment mechanism, this paper proposes
Artificial Neural
Network (ANN) for Non-linear adjustment to ABE (NABE) with the
learning ability to
approximate complex relationships and incorporating the categorical
features. The proposed
NABE is validated on four real world datasets and compared against the
linear adjusted
ABEs, CART, ANN and SWR. Subsequently, eight artificial datasets are
generated for a
systematic investigation on the relationship between model accuracies
and dataset
properties. The comparisons and analysis show that non-linear
adjustment could generally
extend ABE’s flexibility on complex datasets with large number of
categorical features and
improve the accuracies of adjustment techniques.