Software maintainability prediction using an enhanced random forest algorithm

Abstract

Now-a-days, software maintainability has become one of the significant quality-assessing attributes for any software system. Current study presents an enhanced-RFA (Random Forest Algorithm) approach for Software Maintainability Prediction. The proposed approach combines Random Forest (RF) algorithm with three prevalent feature selection techniques namely Chi-Squared, RF and Linear Correlation Filter along with a re-sampling technique intended to improve the prediction accuracy of basic RF algorithm. Enhanced- RFA is applied on two commercially available datasets, QUES and UIMS and performance is evaluated on the basis of R2. Results indicate that the proposed approach performs significantly better than RFA for both the datasets with an improvement in R2 values equal to 69.50%, 65.57% & 69.40% for QUES and 31.90%, 44.94% & 51.81% for UIMS using chisquared, RF and linear correlation filter techniques respectively.

Subject Classification:

• Primary 68N30
• Secondary 68T01

Keywords:

• Maintainability Prediction
• Machine Learning
• Enhanced-RFA
• Feature Selection