  Software cost estimation is one of the prerequisite managerial activities
carried out at the software development initiation stages and also repeated
throughout the whole software life-cycle so that amendments to the total cost
are made. In software cost estimation typically, a selection of project
attributes is employed to produce effort estimations of the expected human
resources to deliver a software product. However, choosing the appropriate
project cost drivers in each case requires a lot of experience and knowledge on
behalf of the project manager which can only be obtained through years of
software engineering practice. A number of studies indicate that popular
methods applied in the literature for software cost estimation, such as linear
regression, are not robust enough and do not yield accurate predictions.
Recently the dual variables Ridge Regression (RR) technique has been used for
effort estimation yielding promising results. In this work we show that results
may be further improved if an AI method is used to automatically select
appropriate project cost drivers (inputs) for the technique. We propose a
hybrid approach combining RR with a Genetic Algorithm, the latter evolving the
subset of attributes for approximating effort more accurately. The proposed
hybrid cost model has been applied on a widely known high-dimensional dataset
of software project samples and the results obtained show that accuracy may be
increased if redundant attributes are eliminated.
