Application of Ant Colony Optimization Techniques to Predict Software Cost Estimation

  • V. Venkataiah
  • Ramakanta Mohanty
  • J. S. Pahariya
  • M Nagaratna
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 5)

Abstract

In modern society, machine learning techniques employed to predict Software Cost Estimation viz. Decision Tree, K-Nearest Neighbor, Support Vector Machine, Neural Networks, and Fuzzy Logic and so on. Every technique has contributed good work in the significant field of software cost estimation. The Computational Intelligence techniques also contributed a great extent in standard-alone. Still there is an immense scope to apply optimization techniques. In this paper, we propose Ant colony optimization techniques to predict software cost estimation based on three datasets collected from literature. For each datasets, we performed tenfold cross validation on International Software Benchmarking Standards Group (ISBSG) dataset and threefold cross validation performed on IBM Data Processing Service (IBMDPS) and COCOMO 81 datasets. The method is validated with real datasets using Root Mean Square Error (RMSE).

Keywords

Software Cost Estimation (SEC) Ant Colony Optimization Technique (ACOT) Travelling Sales Person (TSO) Root Mean Square Error (RMSE) 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • V. Venkataiah
    • 1
  • Ramakanta Mohanty
    • 2
  • J. S. Pahariya
    • 3
  • M Nagaratna
    • 4
  1. 1.Computer Science and EngineeringCMR College of Engineering and TechnologyMedchalIndia
  2. 2.Computer Science and EngineeringKeshav Memorial Institute of TechnologyNarayanagudaIndia
  3. 3.Computer Science and EngineeringRustamji Institute of TechnologyTekanpurIndia
  4. 4.Computer Science and EngineeringJNTUH College of EngineeringKukatpallyIndia

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