Applying Machine Learning to Solve an Estimation Problem in Software Inspections

Authors:
Thomas Ragg;Frank Padberg;Ralf Schoknecht
Affiliations:
-;-;-
Venue:
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Year:
2002

Citing 10
Cited 1

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Abstract

We use Bayesian neural networktec hniques to estimate the number of defects in a software document based on the outcome of an inspection of the document. Our neural networks clearly outperform standard methods from software engineering for estimating the defect content. We also show that selecting the right subset of features largely improves the predictive performance of the networks.