Analogy-based effort estimation: a new method to discover set of analogies from dataset characteristics

TL;DR

This paper introduces a novel analogy-based effort estimation method that uses Bisecting k-medoids clustering to identify the most suitable set of analogies for each project, improving estimation accuracy.

Contribution

It presents a new technique leveraging Bisecting k-medoids clustering to dynamically select analogies per project, enhancing ABE performance over traditional static methods.

Findings

Improved estimation accuracy with the proposed method.

Better understanding of dataset characteristics.

Promising performance results compared to regular ABE models.

Abstract

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