Abstract Vulnerabilities in the source code of the software are critical issues in the realm of software engineering. Coping with vulnerabilities in software source code is becoming more challenging due to several aspects such as complexity and volume. Deep learning has gained popularity throughout the years as a means of addressing such issues. This paper proposes an evaluation of vulnerability detection performance on source code representations and evaluates how machine learning (ML) strategies can improve them. The structure of our experiment consists of three deep neural networks (DNNs) in conjunction with five different source code representations: abstract syntax trees (ASTs), code gadgets (CGs), semantics‐based vulnerability candidates (SeVCs), lexed code representations (LCRs), and composite code representations (CCRs). Experimental results show that employing different ML strategies in conjunction with the base model structure influences the performance results to a varying degree. However, ML‐based techniques suffer from poor performance on class imbalance handling and dimensionality reduction when used in conjunction with source code representations.

Identifying and fixing bug reports with tactical vul-nerabilities in a timely and accurate manner is essential to ensure the security of the software architecture. Manually identifying the bug reports with tactical vulnerabilities is labor-intensive and challenging. This paper presents Itactivul, an approach to automatically identify bug reports with tactical vulnerabilities and recommend their tactical categories to guide the fix. Unlike the existing security bug report prediction approach, we are the first attempt to use deep learning to mine discriminative tactical text features only from the vulnerability descriptions of the National Vulnerability Database (NVD) and apply them to identify bug reports with tactical vulnerabilities. We evaluate Itactivul on three bug reports datasets gathered from three large-scale open-source projects, including Chromium, PHP, and Thunderbird. The experimental results show that Itactivul outperforms baselines by an average of 8.88 %, 13.58 %, and 6.61 % in the F1-score of three datasets, respectively. To improve the explainability of the features mined by Itactivul, we manually analyze the high-weight phrases extracted by using attention backtracking. The results show that Itactivul can mine key and potential tactical vulnerabilities text features.

Vulnerabilities in the source code of software are critical issues in the realm of software engineering. Coping with vulnerabilities in software source code is becoming more challenging due to several aspects of complexity and volume. Deep learning has gained popularity throughout the years as a means of addressing such issues. In this paper, we propose an evaluation of vulnerability detection performance on source code representations and evaluate how Machine Learning (ML) strategies can improve them. The structure of our experiment consists of 3 Deep Neural Networks (DNNs) in conjunction with five different source code representations; Abstract Syntax Trees (ASTs), Code Gadgets (CGs), Semantics-based Vulnerability Candidates (SeVCs), Lexed Code Representations (LCRs), and Composite Code Representations (CCRs). Experimental results show that employing different ML strategies in conjunction with the base model structure influences the performance results to a varying degree. However, ML-based techniques suffer from poor performance on class imbalance handling when used in conjunction with source code representations for software vulnerability detection.

Vulnerabilities in software source code are one of the critical issues in the realm of software code auditing. Due to their high impact, several approaches have been studied in the past few years to mitigate the damages from such vulnerabilities. Among the approaches, deep learning has gained popularity throughout the years to address such issues. In this literature survey, the authors provide an extensive review of the many works in the field software vulnerability analysis that utilise deep learning-based techniques. The reviewed works are systemised according to their objectives (i.e. the type of vulnerability analysis aspect), the area of focus (i.e. the focus area of the analysis), what information about source code is used (i.e. the features), and what deep learning techniques they employ (i.e. what algorithm is used to process the input and produce the output). They also study the limitations of the papers and topical trends concerning vulnerability analysis.