1 research outputs found
Analogy-based effort estimation: a new method to discover set of analogies from dataset characteristics
Analogy-based effort estimation (ABE) is one of the efficient methods for
software effort estimation because of its outstanding performance and
capability of handling noisy datasets. Conventional ABE models usually use the
same number of analogies for all projects in the datasets in order to make good
estimates. The authors' claim is that using same number of analogies may
produce overall best performance for the whole dataset but not necessarily best
performance for each individual project. Therefore there is a need to better
understand the dataset characteristics in order to discover the optimum set of
analogies for each project rather than using a static k nearest projects.
Method: We propose a new technique based on Bisecting k-medoids clustering
algorithm to come up with the best set of analogies for each individual project
before making the prediction. Results & Conclusions: With Bisecting k-medoids
it is possible to better understand the dataset characteristic, and
automatically find best set of analogies for each test project. Performance
figures of the proposed estimation method are promising and better than those
of other regular ABE model