pLatform for Analysis of Resilient and secUre Software
LAZARUS (pLatform for Analysis of Resilient and secUre Software) is a three-year research and innovation project that aims to heal many of the security issues that befall modern software during its development life cycle. The recently introduced paradigm of DevSecOps - in medium to large companies - unfortunately, lacks automated security tools, while most existing solutions are targeting only one narrow step of the SDLC process but miss a much-needed holistic overview of the global security solution. LAZARUS innovates by intervening in multiple steps of the SDLC, performing targeted security checks and collecting valuable information and intelligence from each step, and exploiting advanced ML and AI methods to convert this intelligence into actionable insights and recommendations.
Framework
Design, develop, test, and validate a novel intelligent framework for the development of secure applications
Self-healing
Automatically apply self-healing to a system which undergoes an attack
Vulnerability management
Develop new methods for discovering vulnerabilities in an information system
AI/ML
Integrate artificial and machine learning in tools to automate security checks in DevSecOps
Start date | 1.9.2022 | |
End date | 31.8.2025 | |
Duration | 36 Months | |
Keywords | self-healing; automated vulnerability identification; vulnerability patching |
Funding Framework | Horizon Europe | |
Call | HORIZON-CL3-2021-CS-01-03 | |
 | This project has received funding from the European Union’s Horizon Research and Innovation Programme under Grant Agreement nº 101070303. |
Assessing the Robustness of Test Selection Methods for Deep Neural Networks
Hu Q., Guo Y., Xie X., Cordy M., Ma W., Papadakis M., Ma L., Traon Y.L.E.
ACM Transactions on Software Engineering and Methodology, vol. 34, n° 7, art. no. 195, 2025
Variable Renaming-Based Adversarial Test Generation for Code Model: Benchmark and Enhancement
Wen J., Hu Q., Guo Y., Cordy M., Traon Y.L.
ACM Transactions on Software Engineering and Methodology, vol. 35, n° 1, art. no. 6, 2025
Boosting source code learning with text-oriented data augmentation: an empirical study
Dong Z., Hu Q., Guo Y., Zhang Z., Cordy M., Papadakis M., Le Traon Y., Zhao J.
Empirical Software Engineering, vol. 30, n° 3, art. no. 68, 2025
A Multi-dataset Evaluation of Models for Automated Vulnerability Repair
Khan Z.A., Garg A., Tang Q.
Lecture Notes in Computer Science, vol. 15994 LNCS, pp. 73-87, 2025
Guo Y., Bettaieb S., Casino F.
International Journal of Information Security, vol. 23, n° 5, pp. 3311-3327, 2024
Active Code Learning: Benchmarking Sample-Efficient Training of Code Models
Hu Q., Guo Y., Xie X., Cordy M., Ma L., Papadakis M., Traon Y.L.
IEEE Transactions on Software Engineering, vol. 50, n° 5, pp. 1080-1095, 2024
Test Optimization in DNN Testing: A Survey
Hu Q., Guo Y., Xie X., Cordy M., Ma L., Papadakis M., Traon Y.L.
ACM Transactions on Software Engineering and Methodology, vol. 33, n° 4, art. no. 111, 2024
Poster: Automated Dependency Mapping for Web API Security Testing Using Large Language Models
Li W., Guo Y.
Ccs 2024 Proceedings of the 2024 ACM Sigsac Conference on Computer and Communications Security, pp. 5024-5026, 2024
Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection
Guo Y., Patsakis C., Hu Q., Tang Q., Casino F.
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, vol. 14982 LNCS, pp. 271-289, 2024
An Empirical Study of the Imbalance Issue in Software Vulnerability Detection
Guo Y., Hu Q., Tang Q., Traon Y.L.
Lecture Notes in Computer Science, vol. 14347 LNCS, pp. 371-390, 2024
MUTEN: Mutant-Based Ensembles for Boosting Gradient-Based Adversarial Attack
Hu Q., Guo Y., Cordy M., Papadakis M., Traon Y.L.
Proceedings 2023 38th IEEE ACM International Conference on Automated Software Engineering Ase 2023, pp. 1708-1712, 2023
An Investigation of Quality Issues in Vulnerability Detection Datasets
Guo Y., Bettaieb S.
Proceedings 8th IEEE European Symposium on Security and Privacy Workshops Euro S and Pw 2023, pp. 29-33, 2023
Evaluation of Multi-Language Vulnerability Patching with Pre-Trained Models
Khan Z.A., Garg A., Guo Y., Tang Q.
Proceedings of the ACM Symposium on Applied Computing, pp. 1820-1829, 2026
