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Applying Machine Learning to Solve an Estimation Problem in Software Inspections

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Artificial Neural Networks — ICANN 2002 (ICANN 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2415))

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Abstract

We use Bayesian neural network techniques 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.

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Ragg, T., Padberg, F., Schoknecht, R. (2002). Applying Machine Learning to Solve an Estimation Problem in Software Inspections. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_84

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  • DOI: https://doi.org/10.1007/3-540-46084-5_84

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44074-1

  • Online ISBN: 978-3-540-46084-8

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