Predicting Software Development Effort Using Tuned Artificial Neural Network

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 33)


Software development effort prediction using fixed mathematical formulae is inadequate due to impreciseness and nonlinearity exist in the software project data and leads to high prediction error rate, on the other hand Artificial Neural Network (ANN) techniques are very popular for prediction of software development effort due to its capability to map non linear input with output. This paper explores Error Back Propagation Network (EBPN) for software development effort prediction, by tuning some algorithm specific parameters like learning rate and momentum. EBPN is trained with two benchmark data sets: China and Maxwell. Results are analyzed in terms of various measures and found to be satisfactory.


Software effort prediction Error back propagation network (EBPN) 


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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Guru Ghasidas Vishwavidyalaya Bilaspur (C.G.)BilaspurIndia
  2. 2.Dr. C.V. Raman University Kota Bilaspur (C.G.)BilaspurIndia
  3. 3.Government Engineering College Bilaspur (C.G.)BilaspurIndia

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