Year: 2014
Type: preprint
Abstract: Despite the predictive performance of Analogy-Based Estimation (ABE) in generating better effort estimates, there is no consensus on how to predict the best number of analogies, and which adjustment technique produces better estimates. This paper proposes a new adjusted ABE model based on optimizing and approximating complex relationships between features and reflects that approximation on the final estimate. The results show that the predictive performance of ABE has noticeably been improved, and the number of analogies was remarkably variable for each test project.
Source: arXiv (Cornell University)
Institution: Applied Science Private University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2951213291