Model Tree Based Adaption Strategy for Software Effort Estimation by Analogy

Background: Adaptation technique is a crucial task for analogy based estimation. Current adaptation techniques often use linear size or linear similarity adjustment mechanisms which are often not suitable for datasets that have complex structure with many categorical attributes. Furthermore, the use of nonlinear adaptation technique such as neural network and genetic algorithms needs many user interactions and parameters optimization for configuring them (such as network model, number of neurons, activation functions, training functions, mutation, selection, crossover, etc.). Aims: In response to the abovementioned challenges, the present paper proposes a new adaptation strategy using Model Tree based attribute distance to adjust estimation by analogy and derive new estimates. Using Model Tree has an advantage to deal with categorical attributes, minimize user interaction and improve efficiency of model learning through classification. Method: Seven well known datasets have been used with 3-Fold cross validation to empirically validate the proposed approach. The proposed method has been investigated using various K analogies from 1 to 3. Results: Experimental results showed that the proposed approach produced better results when compared with those obtained by using estimation by analogy based linear size adaptation, linear similarity adaptation, 'regression towards the mean' and null adaptation. Conclusions: Model Tree could form a useful extension for estimation by analogy especially for complex data sets with large number of categorical attributes.