Improving neural network models of defect content in complex software systems

David L. Lanning; Taghi M. Khoshgoftaar; Peter J. Guasti

doi:10.1117/12.235963

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22 March 1996 Improving neural network models of defect content in complex software systems

David L. Lanning, Taghi M. Khoshgoftaar, Peter J. Guasti

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Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235963
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

Accurately predicting the number of defects in program modules is a major problem in the quality control of large software development efforts. With good estimates early in the software development cycle, software engineers can take actions to avoid or prepare for emerging quality problems. Some source code measures are closely related to the distribution of defects across program modules. Using these relationships, software engineers develop models that provide early defect content estimates. Work with neural network based models has demonstrated their utility for this purpose. In this paper, we expand upon early neural network results for predicting the number of defects in program modules.

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David L. Lanning, Taghi M. Khoshgoftaar, and Peter J. Guasti "Improving neural network models of defect content in complex software systems", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235963

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