A comparison of case-based reasoning approaches

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Abstract
Over the years software engineering researchers have suggested numerous techniques for estimating development effort. These techniques have been classified mainly as algorithmic, machine learning and expert judgement. Several studies have compared the prediction accuracy of those techniques, with emphasis placed on linear regression, stepwise regression, and Case-based Reasoning (CBR). To date no converging results have been obtained and we believe they may be influenced by the use of the same CBR configuration.The objective of this paper is twofold. First, to describe the application of case-based reasoning for estimating the effort for developing Web hypermedia applications. Second, comparing the prediction accuracy of different CBR configurations, using two Web hypermedia datasets.Results show that for both datasets the best estimations were obtained with weighted Euclidean distance, using either one analogy (dataset 1) or 3 analogies (dataset 2). We suggest therefore that case-based reasoning is a candidate technique for effort estimation and, with the aid of an automated environment, can be applied to Web hypermedia development effort prediction.