Please use this identifier to cite or link to this item:
Title: Reliable confidence intervals for software effort estimation
Authors: Andreou, Andreas S. 
Papadopoulos, Harris 
Papatheocharous, Efi 
Category: Electrical Engineering - Electronic Engineering - Information Engineering
Field: Engineering and Technology
Issue Date: 1-Dec-2009
Source: CEUR Workshop Proceedings
Abstract: This paper deals with the problem of software effort estimation through the use of a new machine learning technique for producing reliable confidence measures in predictions. More specifically, we propose the use of Conformal Predictors (CPs), a novel type of prediction algorithms, as a means for providing effort estimations for software projects in the form of predictive intervals according to a specified confidence level. Our approach is based on the well-known Ridge Regression technique, but instead of the simple effort estimates produced by the original method, it produces predictive intervals that satisfy a given confidence level. The results obtained using the proposed algorithm on the COCOMO, Desharnais and ISBSG datasets suggest a quite successful performance obtaining reliable predictive intervals which are narrow enough to be useful in practice.
ISSN: 16130073
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

Show full item record

Page view(s)

Last Week
Last month
checked on Jun 26, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.