An investigation of machine learning based prediction systems

Carolyn Mair, Gada Kadoda, Martin Lefley, Keith Phalp, Chris Schofield, Martin Shepperd, Steve Webster

Research output: Contribution to journalArticlepeer-review

Abstract

Traditionally, researchers have used either o?f-the-shelf models such as COCOMO, or developed local models using statistical techniques such as stepwise regression, to obtain software eff?ort estimates. More recently, attention has turned to a variety of machine learning methods such as artifcial 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 e?ort 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 eff?ort 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 enduser and the prediction system, and also to facilitate configuration, particularly of ANNs.
Original languageEnglish
Pages (from-to)23-29
Number of pages7
JournalJournal of Systems and Software
Volume53
Issue number1
Publication statusPublished - 2000
Externally publishedYes

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