Dynamic software maintenance effort estimation modeling using neural network, rule engine and multi-regression approach

  • Authors:

  • Ruchi Shukla;Mukul Shukla;A. K. Misra;T. Marwala;W. A. Clarke

  • Affiliations:

  • Department of Electrical & Electronic Engineering Science, University of Johannesburg, South Africa;Department of Mechanical Engineering Technology, University of Johannesburg, South Africa,Department of Mechanical Engineering, Motilal Nehru National Institute of Technology, Allahabad, India;Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, India;Faculty of Engineering and Built Environment, University of Johannesburg, South Africa;Department of Electrical & Electronic Engineering Science, University of Johannesburg, South Africa

  • Venue:
  • ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part IV
  • Year:
  • 2012

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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.