Software Project Effort Estimation By Analogy

Software effort estimation by analogy is a viable alternative method to other estimation techniques, and in many cases, researchers found it outperformed other estimation methods in terms of accuracy and practitioners acceptance. However, the overall performance of analogy based estimation depends on two major factors: similarity measure and attribute selection & weighting. Current similarity measures such as nearest neighborhood techniques have been criticized that have some inadequacies related to attributes relevancy, noise and uncertainty in addition to the problem of using categorical attributes. This research focuses on improving the efficiency and flexibility of analogy-based estimation to overcome the abovementioned inadequacies. Particularly, this thesis proposes two new approaches to model and handle uncertainty in similarity measurement method and most importantly to reflect the structure of dataset on similarity measurement using Fuzzy models and Fuzzy numbers in addition to Feature Subset Selection.

Autorentext

Mohammad Y. Azzeh is an assistant professor of Software Engineering at Applied Science University. He holds PhD in computing from University of Bradford, UK and MSc in Software Engineering from University of the West of England, UK. He is currently working as a faculty staff member in software engineering department at Applied Science University.