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Rough Neural Network for Software Change Prediction

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Rough Sets and Current Trends in Computing (RSCTC 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2475))

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Abstract

This paper focuses on calibrating a rough neural network based on software complexity measurements and the corresponding number of changes required to bring a software product (either during development or during post-deployment) into compliance with project standards. A good predictive model for software maintenance that can estimate the number of changes that will allow the early identification of modules that are most likely to require extensive modifications. The results reported in this paper are limited to assessing prediction accuracy based on software engineering data obtained during product development. The Rough Set Exploration System (RSES) is used to derive training and testing sets that are used both by RSES and by a rough neural network toolset named MBnet to predict the number of software module changes needed to bring a module intro compliance with project standards. A comparison between MBnet and RSES in predicting the number of changes for a particular software module is also given.

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Author information

Authors and Affiliations

  1. Department of Business Computing, University of Winnipeg, Winnipeg, Manitoba, Canada, R3B 2E9

    Sheela Ramanna

Authors
  1. Sheela Ramanna
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Editor information

Editors and Affiliations

  1. Penn State Great Valley School of Professional Graduate Studies, 30 East Swedesford Road, Malvern, PA, 19355, USA

    James J. Alpigini

  2. Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, R3T 5V6, Canada

    James F. Peters

  3. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  4. Department of Systems and Information Engineering, Maebashi Institute of Technology, 460-1 Kamisadori-Cho, Maebashi-City, 371-0816, Japan

    Ning Zhong

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© 2002 Springer-Verlag Berlin Heidelberg

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Ramanna, S. (2002). Rough Neural Network for Software Change Prediction. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds) Rough Sets and Current Trends in Computing. RSCTC 2002. Lecture Notes in Computer Science(), vol 2475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45813-1_80

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  • DOI: https://doi.org/10.1007/3-540-45813-1_80

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44274-5

  • Online ISBN: 978-3-540-45813-5

  • eBook Packages: Springer Book Archive

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