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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References
Basili, Green, Laitenberger, Lanubile, Shull, Sorumgard, Zelkowitz: “The Empirical Investigation of Perspective-Based Reading”, Empirical Software Engineering 1:2 (1996) 133–164
Biffl, Grossmann: “Evaluating the Accuracy of Defect Estimation Models Based on Inspection Data From Two Inspection Cycles”, Proceedings International Conference on Software Engineering ICSE 23 (2001) 145–154
Bishop: Neural Networks for Pattern Recognition. Oxford Press, 1995
Briand, El-Emam, Freimut, Laitenberger: “A Comprehensive Evaluation of Capture-Recapture Models for Estimating Software Defect Content”, IEEE Transactions on Software Engineering 26:6 (2000) 518–540
Cover, Thomas: Elements of Information Theory. Wiley, 1991
Eick, Loader, Long, Votta, Vander Wiel: “Estimating Software Fault Content Before Coding”, Proceedings International Conference on Software Engineering ICSE 14 (1992) 59–65
Gilb, Graham: Software Inspection. Addison-Wesley, 1993
Khoshgoftaar, Szabo: “Using Neural Networks to Predict Software Faults During Testing”, IEEE Transactions on Reliability 45:3 (1996) 456–462
Mac Kay: “A practical bayesian framework for backpropagation networks”, Neural Computation 4:3 (1992) 448–472
Ragg, Menzel, Baum, Wigbers: “Bayesian learning for sales rate prediction for thousands of retailers”, Neurocomputing 43 (2002) 127–144
Riedmiller: “Supervised learning in multilayer perceptrons-from backpropagation to adaptive learning techniques”, International Journal of Computer Standards and Interfaces 16 (1994) 265–278
Runeson, Wohlin: “An Experimental Evaluation of an Experience-Based Capture-Recapture Method in Software Code Inspections”, Empirical Software Engineering 3:3 (1998) 381–406
Silverman: Density Estimation for Statistics and Data Analysis. Chapman and Hall, 1986
Vander Wiel, Votta: “Assessing Software Designs Using Capture-Recapture Methods”, IEEE Transactions on Software Engineering 19:11 (1993) 1045–1054
Wohlin, Runeson: “Defect Content Estimations from Review Data”, Proceedings International Conference on Software Engineering ICSE 20 (1998) 400–409
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© 2002 Springer-Verlag Berlin Heidelberg
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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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