Feature Selection Techniques to Counter Class Imbalance Problem for Aging Related Bug Prediction

Abstract

Aging-Related Bugs (ARBs) occur in long running systems due to error conditions caused because of accumulation of problems such as memory leakage or unreleased files and locks. Aging-Related Bugs are hard to discover during software testing and also challenging to replicate. Automatic identification and prediction of aging related fault-prone files and classes in an object oriented system can help the software quality assurance team to optimize their testing efforts. In this paper, we present a study on the application of static source code metrics and machine learning techniques to predict aging related bugs. We conduct a series of experiments on publicly available dataset from two large open-source software systems: Linux and MySQL. Class imbalance and high dimensionality are the two main technical challenges in building effective predictors for aging related bugs.