Detection of Aberrant Data Points for an effective Effort Estimation using an Enhanced Algorithm with Adaptive Features
S. Malathi and S. Sridhar
DOI : 10.3844/jcssp.2012.195.199
Journal of Computer Science
Volume 8, Issue 2
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.
© 2012 S. Malathi and S. Sridhar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.