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Software development involves a number of interrelated factors which affect development effort and productivity. Since many of these relationships are not well understood, accurate estimation of softare development time and effort is a dificult problem. Most estimation models in use or proposed in the literature are based on regression techniques. This paper examines the potential of two artificial intelligence approaches i.e. artificial neural network and case-based reasoning for creating development effort estimation models.
Artificial neural network can provide accurate estimates when there are complex relationships between variables and where the input data is distorted by high noise levels Case-based reasoning solves problems by adapting solutions from old problems similar to the current problem. This research examines both the performance of back-propagation artificial neural networks in estimating software development effort and the potential of case-based reasoning for development estimation using the same dataset.