An Assessment of Eclipse Bugs' Priority and Severity Prediction Using Machine Learning

The reliability and quality of software programs remains to be an important challenging aspect design. Software developers system operators spend huge time on assessing overcoming expected unexpected errors that might affect the users’ experience negatively. One major concerns in developing problems is bug reports, which contains severity priority these defects. For a long time, this task was performed manually with effort consumptions by operators. Therefore, paper, we present novel automatic assessment tool using Machine Learning algorithms, for bugs’ reports based several features such as hardware, product, assignee, OS, component, target milestone, votes, versions. aim build automatically classifies bugs according makes predictions most representative report text. To perform task, used Multi-Nominal Naive Bayes, Random Forests Classifier, Bagging, Ada Boosting, SVC, KNN, Linear SVM Classifiers Natural Language Processing techniques analyze Eclipse dataset. approach shows promising results detection prediction.