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Estimating software development effort with connectionist models
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DOI：10.1016/S0950-5849(97)00004-9.png)
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Abstract
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Accurate software development effort estimation is important for effective project management. Research studies indicate that effort estimation is a complex issue and results have in general not been encouraging. Artificial Neural Networks are recognised for their ability to provide good results when dealing with problems where there are complex relationships between inputs and outputs, and where the input data is distorted by high noise levels. This paper reports on the assessment of back-propagation neural network models for effort estimation. The models were tested on simulated data as well as actual data of commercial projects. This project data had large productivity variations, noise and missing data values, which enabled model evaluation under typical software development conditions. The results were encouraging, with the networks showing an ability to estimate development effort within 25% of actual effort more than 75% of the time for one large commercial data set. (C) 1997 Elsevier Science B.V.
Keywords:
software estimation
artificial neural networks
metrics
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