Neural Networks and regression

Machine Learning Approaches to Estimating Software Development Effort
Analysis Neural Networks This manuscript discusses the use of machine learning approaches (in particular artificial neural networks and regression trees) to estimate the amount of person-months it would take to accomplish a particular task. The authors grounded their comparisons by considering the results of previous studies on the same data sets. The regression tree method used dichotomous variables selected greedily using change in mean squared error. The ANN used normalized input variables and one layer of hidden nodes.

In general, I was a little surprised to see how wide the data was: 63 projects (+15 tested) with 33 inputs. Not surprisingly, the CartX, even when every training item was left as a leaf, wasn't able to represent the new data particularly well. Presumably some of this was also related to programming language gap: the test data was primarily one language. The continuous nature of the ANN  yielded closer estimates.


When considering the repeated testing, the wildly large amount of variance in the results supports the idea that the training sets were not comprehensive.


Additionally, I wonder about how well the attributes would truly be able to predict the difficulty of the project. Software engineering was never my interest, so there may be some bias in this, but I can't imagine trying to accurately and numerically classify the skill of individuals. This holds true for the aspects of the projects, too: Some domains seem to just be harder than others.

Source: Robert's ML notes

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