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Dynamic Software Maintenance Effort Estimation Modeling Using Neural Network, Rule Engine and Multi-regression Approach

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Computational Science and Its Applications – ICCSA 2012 (ICCSA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7336))

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Abstract

The dynamic business environment of software projects typically involves a large number of technical, demographic and environmental variables. This coupled with imprecise data on human, management and dynamic factors makes the objective estimation of software development and maintenance effort a very challenging task. Currently, no single estimation model or tool has been able to coherently integrate and realistically address the above problems. This paper presents a multi-fold modeling approach using neural network, rule engine and multi-regression for dynamic software maintenance effort estimation. The system dynamics modeling tool developed using quantitative and qualitative inputs from real life projects is able to successfully simulate and validate the dynamic behavior of a software maintenance estimation system.

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Author information

Authors and Affiliations

  1. Department of Electrical & Electronic Engineering Science, University of Johannesburg, South Africa

    Ruchi Shukla & W. A. Clarke

  2. Department of Mechanical Engineering Technology, University of Johannesburg, South Africa

    Mukul Shukla

  3. Department of Mechanical Engineering, Motilal Nehru National Institute of Technology, Allahabad, India

    Mukul Shukla

  4. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, India

    A. K. Misra

  5. Faculty of Engineering and Built Environment, University of Johannesburg, South Africa

    T. Marwala

Authors
  1. Ruchi Shukla
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  2. Mukul Shukla
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  3. A. K. Misra
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  4. T. Marwala
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  5. W. A. Clarke
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Editor information

Editors and Affiliations

  1. Laboratory of Urban and Territorial Systems, University of Basilicata, 10, Viale dell’Ateneo Lucano, 85100, Potenza, Italy

    Beniamino Murgante

  2. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli 1, 06123, Perugia, Italy

    Osvaldo Gervasi

  3. Department of Cyber Security Science, Federal University of Technology, Gidan Kwano Campus, Minna, Nigeria

    Sanjay Misra

  4. Faculty of Engineering, Department of Electronics Engineering and Telecommunications, State University of Rio de Janeiro, Rua Sao Francisco Xavier, 524, 50. andar, sala 5145-F, Maracana, 20, 550-013, Rio de Janeiro, RJ, Brazil

    Nadia Nedjah

  5. Department of Production and Systems, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal

    Ana Maria A. C. Rocha

  6. School of Business Systems, Monash University, 3800, Clayton, VIC, Australia

    David Taniar

  7. Department of Intelligent Informatics, Kyushu Sangyo University, 2-3-1 Matsukadai, Higashi-ku, 813-8503, Fukuoka, Japan

    Bernady O. Apduhan

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© 2012 Springer-Verlag Berlin Heidelberg

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Shukla, R., Shukla, M., Misra, A.K., Marwala, T., Clarke, W.A. (2012). Dynamic Software Maintenance Effort Estimation Modeling Using Neural Network, Rule Engine and Multi-regression Approach. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31128-4_12

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  • DOI: https://doi.org/10.1007/978-3-642-31128-4_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31127-7

  • Online ISBN: 978-3-642-31128-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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