Better Security Bug Report Classification via Hyperparameter Optimization

TL;DR

This paper enhances security bug report classification by applying hyperparameter optimization to data pre-processing and model parameters, significantly improving recall rates in identifying security vulnerabilities.

Contribution

It demonstrates that optimizing data pre-processing methods yields greater improvements than tuning learners alone in classifying security bug reports.

Findings

Data pre-processing optimization improves recall by up to 65%.

Hyperparameter tuning of data pre-processing is more effective than tuning learners.

Significant improvements in security bug report classification accuracy.

Abstract

When security bugs are detected, they should be (a)~discussed privately by security software engineers; and (b)~not mentioned to the general public until security patches are available. Software engineers usually report bugs to bug tracking system, and label them as security bug reports (SBRs) or not-security bug reports (NSBRs), while SBRs have a higher priority to be fixed before exploited by attackers than NSBRs. Yet suspected security bug reports are often publicly disclosed because the mislabelling issues ( i.e., mislabel security bug reports as not-security bug report). The goal of this paper is to aid software developers to better classify bug reports that identify security vulnerabilities as security bug reports through parameter tuning of learners and data pre-processor. Previous work has applied text analytics and machine learning learners to classify which reported bugs are security related. We improve on that work, as shown by our analysis of five open source projects. We apply hyperparameter optimization to (a)~the control parameters of a learner; and (b)~the data pre-processing methods that handle the case where the target class is a small fraction of all the data. We show that optimizing the pre-processor is more useful than optimizing the learners. We also show that improvements gained from our approach can be very large. For example, using the same data sets as recently analyzed by our baseline approach, we show that adjusting the data pre-processing results in improvements to classification recall of 35% to 65% (median to max) with moderate increment of false positive rate.