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

R. Shukla, M. Shukla, A.K. Misra, T. Marwala, W.A. Clarke

Research output: Contribution to journalArticle

8 Citations (Scopus)


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. © 2012 Springer-Verlag.
Original languageEnglish
Pages (from-to)157-169
Number of pages13
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7336 LNCS
Issue numberPART 4
Publication statusPublished - 2012
Externally publishedYes

Bibliographical note

cited By 2; Conference of 12th International Conference on Computational Science and Its Applications, ICCSA 2012 ; Conference Date: 18 June 2012 Through 21 June 2012; Conference Code:90945


  • Dynamic behaviors
  • Dynamic business environment
  • Dynamic factors
  • Effort Estimation
  • Environmental variables
  • Estimation models
  • Estimation systems
  • Imprecise data
  • Modeling approach
  • Multi-regression
  • regression
  • Rule engine
  • Software project
  • System Dynamics
  • System dynamics modeling, Neural networks
  • Regression analysis
  • System theory, Computer software maintenance

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