Research Article Open Access

Detection of Aberrant Data Points for an effective Effort Estimation using an Enhanced Algorithm with Adaptive Features

S. Malathi1 and S. Sridhar1
  • 1 ,
Journal of Computer Science
Volume 8 No. 2, 2012, 195-199

DOI: https://doi.org/10.3844/jcssp.2012.195.199

Submitted On: 11 October 2011 Published On: 21 November 2011

How to Cite: Malathi, S. & Sridhar, S. (2012). Detection of Aberrant Data Points for an effective Effort Estimation using an Enhanced Algorithm with Adaptive Features. Journal of Computer Science, 8(2), 195-199. https://doi.org/10.3844/jcssp.2012.195.199

Abstract

Problem statement: The spiraling growth of IT industry has witnessed an unprecedented change in the software development paradigm, from algorithmic models to machine learning techniques. At present, there are no standard methods to predict the accuracy of software cost estimation, which is an important goal of the software community. Approach: This study proposes a simple and systematic algorithmic procedure for analogy based software cost prediction to detect the aberrant data points. The algorithm is analyzed and correlated with the Desharnais and NASA datasets containing all adaptive features with numerical and categorical variables. Results: The interpreted curves using the above datasets depict a discernible anomaly for the dataset having more categorical variables, thereby indicating the erroneous data points. Conclusion: The elimination of aberrant data points using the new algorithmic method improves the accuracy of software cost estimation using historical data sets.

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Keywords

  • algorithmic method
  • cost prediction
  • categorical variables
  • adaptive features
  • aberrant datapoints