Assessing Cross-Project Technique for Software Maintainability Prediction

Abstract

Software Maintainability refers to the ease with which software maintenance activities like correction of faults, deletion of obsolete code, addition of new code etc. can be carried out to adapt to the modified environment. Predicting maintainability in early stages of development helps in reducing the cost of maintenance and ensures optimum utilization of resources. Sometimes, it becomes difficult to train prediction models using historical data of the same dataset for which the model is being developed because of the unavailability of sufficient amount of training data, in turn making a way for Cross-Project technique for Software Maintainability Prediction (CPSMP). In order to evaluate the proposed CPSMP technique, QUES dataset is used as training set and UIMS dataset is used as test set in this study with 19 different regression modelling methods. Performance of CPSMP model is evaluated using Root Mean Square Error (RMSE) as an accuracy measure. Results show that cross-project technique can successfully be applied for maintainability prediction. The average RMSE value calculated for all the modelling methods is found to be 82.310 without CPSMP whereas an average RMSE value of 71.532 is obtained with CPSMP resulting in an overall improvement in prediction performance by 13.09%. Also, 84.21% of the total techniques used in this study performed better with CPSMP.