Combining Software Metrics and Text Features for Vulnerable File Prediction

• Main Authors: ZHANG, Yun, David LO, XIA, Xin, XU, Bowen, SUN, Jianling Sun, LI, Shanping
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: Machine Learning; Text Mining; Vulnerable File; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/3097
• Institution: Singapore Management University

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.