# Towards an Improved Understanding of Software Vulnerability Assessment Using Data-Driven Approaches

arXiv: https://arxiv.org/abs/2207.11708 · HTML: https://mlsanity.com/paper/2207.11708/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2022.

## Built on by (most cited first, 1 of 1)

- [LLMSecConfig: An LLM-Based Approach for Fixing Software Container Misconfigurations](https://mlsanity.com/paper/2502.02009/) (2025)

## Abstract (arXiv)

The thesis advances the field of software security by providing knowledge and automation support for software vulnerability assessment using data-driven approaches. Software vulnerability assessment provides important and multifaceted information to prevent and mitigate dangerous cyber-attacks in the wild. The key contributions include a systematisation of knowledge, along with a suite of novel data-driven techniques and practical recommendations for researchers and practitioners in the area. The thesis results help improve the understanding and inform the practice of assessing ever-increasing vulnerabilities in real-world software systems. This in turn enables more thorough and timely fixing prioritisation and planning of these critical security issues.