An investigation of machine learning based prediction systems

Authors: Mair, C., Kadoda, G., Lefley, M., Phalp, K., Schofield, C., Shepperd, M. and Webster, S.

Journal: JOURNAL OF SYSTEMS AND SOFTWARE

Volume: 53

Issue: 1

Pages: 23-29

ISSN: 0164-1212

DOI: 10.1016/S0164-1212(00)00005-4

Source: Web of Science (Lite)

Empirical Studies Applied to Software Process Models

Authors: Mair, C., Kadoda, G., Phalp, K.T., Schofield, C., Shepperd, M. and Webster, S.

Journal: Journal of Systems and Software

Volume: 53

Pages: 23-29

ISSN: 0164-1212

DOI: 10.1016/S0164-1212(00)00005-4

Abstract:

Traditionally, researchers have used either off-the-shelf models such as COCOMO, or developed local models using statistical techniques such as stepwise regression, to obtain software effort estimates. More recently, attention has turned to a variety of machine learning methods such as artificial neural networks (ANNs), case-based reasoning (CBR) and rule induction (RI). This paper outlines some comparative research into the use of these three machine learning methods to build software effort prediction systems. We briefly describe each method and then apply the techniques to a dataset of 81 software projects derived from a Canadian software house in the late 1980s. We compare the prediction systems in terms of three factors: accuracy, explanatory value and configurability. We show that ANN methods have superior accuracy and that RI methods are least accurate. However, this view is somewhat counteracted by problems with explanatory value and configurability. For example, we found that considerable effort was required to configure the ANN and that this compared very unfavourably with the other techniques, particularly CBR and least squares regression (LSR). We suggest that further work be carried out, both to further explore interaction between the end-user and the prediction system, and also to facilitate configuration, particularly of ANNs.

http://www.elsevier.com/locate/jss

Source: Manual

Preferred by: Keith Phalp