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

doi:10.1007/978-3-642-31128-4_12

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 journal › Article › peer-review

16 Citations (Scopus)

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. © 2012 Springer-Verlag.

Original language	English
Pages (from-to)	157-169
Number of pages	13
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7336 LNCS
Issue number	PART 4
DOIs	https://doi.org/10.1007/978-3-642-31128-4_12
Publication status	Published - 2012
Externally published	Yes

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

Keywords

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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10.1007/978-3-642-31128-4_12

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84863888241&doi=10.1007%2f978-3-642-31128-4_12&partnerID=40&md5=489e08c145739d3787b0312a85aad330

Cite this

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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7336 LNCS(PART 4), 157-169. https://doi.org/10.1007/978-3-642-31128-4_12

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

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. {\textcopyright} 2012 Springer-Verlag.",

keywords = "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",

author = "R. Shukla and M. Shukla and A.K. Misra and T. Marwala and W.A. Clarke",

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",

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volume = "7336 LNCS",

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Shukla, R, Shukla, M, Misra, AK, Marwala, T & Clarke, WA 2012, 'Dynamic software maintenance effort estimation modeling using neural network, rule engine and multi-regression approach', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7336 LNCS, no. PART 4, pp. 157-169. https://doi.org/10.1007/978-3-642-31128-4_12

Dynamic software maintenance effort estimation modeling using neural network, rule engine and multi-regression approach. / Shukla, R.; Shukla, M.; Misra, A.K. et al.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 7336 LNCS, No. PART 4, 2012, p. 157-169.

Research output: Contribution to journal › Article › peer-review

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AU - Shukla, M.

AU - Misra, A.K.

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AU - Clarke, W.A.

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KW - Environmental variables

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KW - regression

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KW - Software project

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Shukla R, Shukla M, Misra AK, Marwala T, Clarke WA. Dynamic software maintenance effort estimation modeling using neural network, rule engine and multi-regression approach. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2012;7336 LNCS(PART 4):157-169. doi: 10.1007/978-3-642-31128-4_12

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

Abstract

Bibliographical note

Keywords

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Cite this