Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/7249
Title: The scientific basis for prediction research
Authors: Shepperd, M
Issue Date: 2012
Publisher: Association for Computing Machinery
Citation: In Proceedins of PROMISE '12, the 8th International Conference on Predictive Models in Software Engineering, Lund, Sweden, pp. 1 - 2, Keynote Speech, 21-22 Sep 2012
Abstract: In recent years there has been a huge growth in using statistical and machine learning methods to find useful prediction systems for software engineers. Of particular interest is predicting project effort and duration and defect behaviour. Unfortunately though results are often promising no single technique dominates and there are clearly complex interactions between technique, training methods and the problem domain. Since we lack deep theory our research is of necessity experimental. Minimally, as scientists, we need reproducible studies. We also need comparable studies. I will show through a meta-analysis of many primary studies that we are not presently in that situation and so the scientific basis for our collective research remains in doubt. By way of remedy I will argue that we need to address these issues of reporting protocols and expertise plus ensure blind analysis is routine.
Description: Copyright @ 2012 ACM
URI: http://dl.acm.org/citation.cfm?id=2365326
http://bura.brunel.ac.uk/handle/2438/7249
DOI: http://dx.doi.org/10.1145/2365324.2365326
ISBN: 978-1-4503-1241-7
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf97.24 kBUnknownView/Open


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