🎉 Exciting News!

📚 Our work 'Are Latent Vulnerabilities Hidden Gems for Software Vulnerability Prediction? An Empirical Study' authored by Triet Le @lhmtriet, Xiaoning Du and Ali Babar @alibabar has been accepted in Mining Software Repositories (MSR) 2024 ! 🚀🔒

🔍 We studies on the latent vulnerable functions in two commonly used Software Vulnerability (SV) datasets and their utilization for function level and line-level SV predictions! Our large-scale study using the SZZ algorithm uncovered 100k+ latent vulnerable functions, boosting SV predictions by 4× on average.

🚧 This work demonstrates the positive impacts of using SZZ-based latent SVs in the studied datasets for SV prediction. Our state-of-the-art SV prediction model shows a remarkable 24.5% increase in function-level SV prediction performance and a whopping 67% improvement in localizing vulnerable lines.

🌐 Bridging gaps, enhancing datasets, and fortifying SV prediction tasks – a promising step towards a more secure future! 🛡️💻

📜Full paper available at: https://arxiv.org/abs/2401.11105 🔖 Data and code is available at: https://github.com/lhmtriet/Latent-Vulnerability

#CyberSecurity #Research #SVPrediction #DataScience #Innovation #InfoSec #TechBreakthroughs #PaperPublished