An investigation of machine learning based prediction systems

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

Formal software development tools: an investigation into usability

Formal methods are techniques that are firmly based in mathematics, they can be used to specify and verify computer systems. Formal techniques offer many advantages, including correctness and productivity over less formal ones. Wide acceptance of these methods is hindered by their relatively difficult notations and theories. This thesis takes the view that the availability of usable tools that support formal techniques plays an important role in promoting their use by a wider community of software engineers. [Continues.

Token-by-token syntax-directed editing (using an LR parser)

This paper demonstrates a new approach to the building of a syntax-directed editor (SDE). The approach does not force the user to adopt a top-down syntax-oriented view of editing but supports the traditional text-editing approach of deciding which token to write next. Choices are made from menus listing only the tokens that are syntactically legal at a given point. The actual menus used by the editor are created by reference to the tables used by an LALR parser-generator

Experiences Using Case-Based Reasoning to Predict Software Project Effort

This paper explores some of the practical issues associated with the use of case-based reasoning (CBR) or estimation by analogy. We note that different research teams have reported widely differing results with this technology. Whilst we accept that underlying characteristics of the datasets being used play a major role we also argue that configuring a CBR system can also have an impact. We examine the impact of the choice of number of analogies when making predictions; we also look at different adaptation strategies. Our analysis is based on a dataset of software projects collected by a Canadian software house. Our results show that choosing analogies is important but adaptation strategy appears to be less so. These findings must be tempered, however, with the finding that it was difficult to show statistical significance for smaller datasets even when the accuracy indicators differed quite substantially. For this reason we urge some degree of caution when comparing competing predicti..