Intelligence System for Software Maintenance Severity Prediction

Abstract

The software industry has been experiencing a software crisis, a difficulty of delivering software within budget, on time, and of good quality. This may happen due to number of defects present in the different modules of the project that may require maintenance. This necessitates the need of predicting maintenance urgency of the particular module in the software. In this paper, we have applied the different predictor models to NASA five public domain defect datasets coded in C, C++, Java and Perl programming languages. Twenty one software metrics of different datasets and Java Classes of thirty five algorithms belonging to the different learner categories of the WEKA project have been evaluated for the prediction of maintenance severity. The results of ten fold cross validation are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for different project datasets. The results show that logistic model Trees (LMT) and Complimentary Naïve Bayes (CNB) based Model provide a relatively better prediction consistency compared to other models and hence, can be used for the maintenance severity prediction of the software. The developed system can also be used for analysis and to evaluate the influence of different factors on the maintenance severity of different software project modules.