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An Explainable Hybrid Learning Framework for Vulnerability Detection to Enhance Software Dependability

https://doi.org/10.2139/ssrn.5782209
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Outgoing claims: the papers this work cites, builds on, or argues with.

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A Comprehensive Review of Static Memory Analysis
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Trustworthy Graph Neural Networks: Aspects, Methods, and Trends
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Dependable computing: From concepts to design diversity
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Modeling and Discovering Vulnerabilities with Code Property Graphs
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A comparative analysis of network dependability, fault-tolerance, reliability, security, and survivability
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Basic concepts and taxonomy of dependable and secure computing
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Software Defect Prediction Based on Deep Representation Learning of Source Code From Contextual Syntax and Semantic Graph
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Dependability Evaluation of Software Systems in Operation
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An Empirical Study on API Usages
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Explaining of Transformer-based Models for Vulnerable Function Detection
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"Why Should I Trust You?"
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A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries
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Relation-aware Graph Convolutional Networks for Multi-relational Network Alignment
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Detecting Condition-Related Bugs with Control Flow Graph Neural Network
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A Systematic Literature Review on Automated Software Vulnerability Detection Using Machine Learning
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SMOTE: Synthetic Minority Over-sampling Technique
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Leveraging Artificial Intelligence for Automated Testing and Quality Assurance in Software Development Lifecycles
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Resilient Systems: Building Secure Cyber-Physical Infrastructure for Critical Industries Against Emerging Threats
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