Title :
Combining Software Metrics and Text Features for Vulnerable File Prediction
Author :
Yun Zhang;David Lo;Xin Xia;Bowen Xu;Jianling Sun;Shanping Li
Author_Institution :
Coll. of Comput. Sci. &
Abstract :
In recent years, to help developers reduce time and effort required to build highly secure software, a number of prediction models which are built on different kinds of features have been proposed to identify vulnerable source code files. In this paper, we propose a novel approach VULPREDICTOR to predict vulnerable files, it analyzes software metrics and text mining together to build a composite prediction model. VULPREDICTOR first builds 6 underlying classifiers on a training set of vulnerable and non-vulnerable files represented by their software metrics and text features, and then constructs a meta classifier to process the outputs of the 6 underlying classifiers. We evaluate our solution on datasets from three web applications including Drupal, PHPMyAdmin and Moodle which contain a total of 3,466 files and 223 vulnerabilities. The experiment results show that VULPREDICTOR can achieve F1 and EffectivenessRatio@20% scores of up to 0.683 and 75%, respectively. On average across the 3 projects, VULPREDICTOR improves the F1 and EffectivenessRatio@20% scores of the best performing state-of-the-art approaches proposed by Walden et al. by 46.53% and 14.93%, respectively.
Keywords :
"Software metrics","Feature extraction","Predictive models","Prediction algorithms","Decision trees","Software algorithms","Training"
Conference_Titel :
Engineering of Complex Computer Systems (ICECCS), 2015 20th International Conference on
DOI :
10.1109/ICECCS.2015.15