Analogy-based effort estimation: a new method to discover set of analogies from dataset characteristics
This work addresses software effort estimation for developers or project managers, but it is incremental as it builds on existing analogy-based methods with a clustering-based improvement.
The authors tackled the problem of software effort estimation by proposing a method to discover an optimal set of analogies for each project based on dataset characteristics, rather than using a static number, resulting in promising and better performance compared to regular analogy-based models.
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 models