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Software Effort Estimation Using Data Mining Techniques

  • Tirimula Rao Benala
  • Rajib Mall
  • P. Srikavya
  • M. Vani HariPriya
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 248)

Abstract

This paper describes an empirical study undertaken to investigate the quantitative aspects of application of data mining techniques to build models for Software effort estimation. The techniques chosen are Multi linear regression, Logistic regression and CART.Empirical evaluation using three fold cross validation procedure has been carried out using three bench marking datasets of software projects, namely, Nasa93, Cocomo81, and Bailey Basili. We observed that: (1) CART technique is suitable for Nasa93 and Nasa93_5. (2). Multiple Linear Regression is suitable for Nasa93_2, Cocomo81s, Cocomo81o and Basili Bailey. (3). Logistic Regression is suitable for Nasa93_1, Cocomo81 and Cocomo81e. It is concluded that data mining techniques tend to help estimating in the best way possible as they are objective and are applicable to unlimited sets of data.

Keywords

Software effort estimation CART Logistic regression 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tirimula Rao Benala
    • 1
  • Rajib Mall
    • 2
  • P. Srikavya
    • 3
  • M. Vani HariPriya
    • 3
  1. 1.Department of Information TechnologyJNTUK, University College of EngineeringVizianagaramIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of TechnologyKharagpurIndia
  3. 3.Department of Computer Science and EngineeringAnil Neerukonda Institute of Technology and SciencesVisakhapatnamIndia

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