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This paper reports experience from the development and use of eleven different software maintenance effort prediction models. The models were developed applying regression analysis, neural networks and pattern recognition and the prediction accuracy was measured and compared for each model type. The most accurate predictions were achieved applying models based on multiple regression and on pattern recognition. We suggest the use of prediction models as instruments to support the expert estimates and to analyse the impact of the maintenance variables on the maintenance process and product. We believe that the pattern recognition based models evaluated, i.e., the prediction models based on the Optimized Set Reduction method, show potential for such use.
Cost models, neural network, pattern recognition, prediction models, regression, software maintenance, software measurement.

M. Jørgensen, "Experience With the Accuracy of Software Maintenance Task Effort Prediction Models," in IEEE Transactions on Software Engineering, vol. 21, no. , pp. 674-681, 1995.
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