ANN model for predicting software function point metric

ABSTRACT

Software Engineering measurement and analysis specially, size estimation initiatives have been in the center of attention for many firms. Function Point (FP) metric is among the most commonly used techniques to estimate the size of software system projects or software systems for measuring the functionality delivered by a system. In this paper we explore an alternative, Artificial Neural Network (ANN) approach for predicting function Point. We proposed an ANN model to explore neural network as tool for function point metric. A multilayer feed forward network is trained using backpropogation algorithm and demonstrated to be suitable. The training and validation data is randomly selected from the data repository of 365 projects [7]. The experimental results of two validation sets each of 55 projects indicate that the Mean Absolute Relative Error (MARE) was 0.198 and 0.145 of ANN model and shows that ANN model is a competitive model as Function Point Metric.

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INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.2 Design Tools and Techniques

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.3 Coding Tools and Techniques
      D.2.8 Metrics

General Terms:
Design, Experimentation, Measurement, Performance

Keywords:
artificial neural network, feed forward backpropogation, function point, mean absolute relative error