Effort Prediction Models Using Self-Organizing Maps for Embedded Software Development Projects

In this paper, we create effort prediction models using self-organizing maps (SOMs) for embedded software development projects. SOMs are a type of artificial neural networks that rely on unsupervised learning. They produce a low-dimensional, discretized representation of the input space of training samples, these representations are called maps. SOMs are useful for visualizing low-dimensional views of high-dimensional data a multidimensional scaling technique. The advantages of using SOMs for statistical applications are as follows: (1) enabling reasonable inferences to be made from incomplete information via association and recollection, (2) visualizing data, (3) summarizing large-scale data, and (4) creating nonlinear models. We focus on the first advantage to create effort prediction models. To verify our approach, we perform an evaluation experiment that compares SOM models to feed forward artificial neural network (FANN) models using Welch's t test. The results of the comparison indicate that SOM models are more accurate than FANN models for the mean of absolute errors when predicting the amount of effort, because mean errors of the SOM are statistically significantly lower.