Functional networks as a novel data mining paradigm in forecasting software development efforts

This paper proposes a new intelligence paradigm scheme to forecast that emphasizes on numerous software development elements based on functional networks forecasting framework. The most common methods for estimating software development efforts that have been proposed in literature are: line of code (LOC)-based constructive cost model (COCOMO), function point (FP) based on neural networks, regression, and case-based reasoning (CBR). Unfortunately, such forecasting models have numerous of drawbacks, namely, their inability to deal with uncertainties and imprecision present in software projects early in the development life-cycle. The main benefit of this study is to utilize both function points and development environments of recent software development cases prominent, which have high impact on the success of software development projects. Both implementation and learning process are briefly proposed. We investigate the efficiency of the new framework for predicting the software development efforts using both simulation and COCOMO real-life databases. Prediction accuracy of the functional networks framework is evaluated and compared with the commonly used regression and neural networks-based models. The results show that the new intelligence paradigm predicts the required efforts of the initial stage of software development with reliable performance and outperforms both regression and neural networks-based models.

Research highlights
► This research proposes functional networks data mining predictive model for the software development efforts.
► Functional networks is a new intelligence paradigm that deals with generalized functional models instead of standard types and then the neuron functions associated with each neuron are not fixed but are learnt from the available data.
► It is a problem-driven data mining predictive model, which means that the initial architecture is designed based on a problem in hand.
► The results of this research indicate that functional networks learning scheme was competitive even better than both standard neural networks and multiple regression.