| Improving function coverage with munch: a hybrid fuzzing and directed symbolic execution approach S Ognawala, T Hutzelmann, E Psallida, A Pretschner SAC '18 Proceedings of the 33rd Annual ACM Symposium on Applied Computing …, 2018 | 74 | 2018 |
| MACKE: Compositional analysis of low-level vulnerabilities with symbolic execution S Ognawala, M Ochoa, A Pretschner, T Limmer Proceedings of the 31st IEEE/ACM International Conference on Automated …, 2016 | 47 | 2016 |
| Automatically assessing vulnerabilities discovered by compositional analysis S Ognawala, RN Amato, A Pretschner, P Kulkarni Proceedings of the 1st International Workshop on Machine Learning and …, 2018 | 22 | 2018 |
| Compositional fuzzing aided by targeted symbolic execution S Ognawala, F Kilger, A Pretschner arXiv preprint arXiv:1903.02981, 2019 | 16 | 2019 |
| Early functional size estimation with IFPUG unit modified. JJ Cuadrado-Gallego, P Rodríguez-Soria, A González, D Castelo, ... IEEE/ACIS 9th International Conference on Computer and Information Science …, 2010 | 14 | 2010 |
| Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings I Mohr, M Krimmel, S Sturua, MK Akram, A Koukounas, M Günther, ... arXiv preprint arXiv:2402.17016, 2024 | 13 | 2024 |
| Fast feedback cycles in empirical software engineering research A Vetrò, S Ognawala, DM Fernández, S Wagner 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering 2 …, 2015 | 11 | 2015 |
| An exploratory survey of hybrid testing techniques involving symbolic execution and fuzzing S Ognawala, A Petrovska, K Beckers arXiv preprint arXiv:1712.06843, 2017 | 8 | 2017 |
| Regularizing Recurrent Networks-On Injected Noise and Norm-based Methods S Ognawala, J Bayer arXiv preprint arXiv:1410.5684, 2014 | 8 | 2014 |
| Jina CLIP: Your CLIP Model Is Also Your Text Retriever A Koukounas, G Mastrapas, M Günther, B Wang, S Martens, I Mohr, ... arXiv preprint arXiv:2405.20204, 2024 | 7 | 2024 |
| Where do we stand in requirements engineering improvement today? First results from a mapping study DM Fernández, S Ognawala, S Wagner, M Daneva Proceedings of the 8th ACM/IEEE International Symposium on Empirical …, 2014 | 7 | 2014 |
| Where do we stand in requirements engineering improvement today? First results from a mapping study D Méndez Fernández, S Ognawala, S Wagner, M Daneva arXiv e-prints, arXiv: 1701.05497, 2017 | 4 | 2017 |
| Illumination compensation and normalization using low-rank decomposition of multispectral images in dermatology A Duliu, R Brosig, S Ognawala, T Lasser, M Ziai, N Navab Information Processing in Medical Imaging: 24th International Conference …, 2015 | 4 | 2015 |
| Reviewing KLEE's Sonar-Search Strategy in Context of Greybox Fuzzing S Ognawala, A Pretschner, T Hutzelmann, E Psallida, RN Amato arXiv preprint arXiv:1803.04881, 2018 | 3 | 2018 |
| ML-based tactile sensor calibration: A universal approach M Karl, A Lohrer, D Shah, F Diehl, M Fiedler, S Ognawala, J Bayer, ... arXiv preprint arXiv:1606.06588, 2016 | 2 | 2016 |
| Scalable Greybox Fuzzing for Effective Vulnerability Management S Ognawala Universitätsbibliothek der Technischen Universität München, 2020 | | 2020 |
| Requirements Engineering Improvement Today: A Systematic Mapping Study; TUM Technische Universität München, Institut Für Informatik S Ognawala, DM Fernández, S Wagner TUM Technische Universität München, Institut für Informatik, 2014 | | 2014 |
| Requirements Engineering Improvement Today S Ognawala, DM Fernández, S Wagner | | 2014 |
| Impact factors for severity assessment of bugs discovered via compositional symbolic execution A Pretschner, S Ognawala | | |
| Presentation: Reviewing KLEE’s Sonar-Search Strategy in Context of Greybox Fuzzing S Ognawala, A Pretschner, T Hutzelmann, E Psallida, RN Amato | | |
Alexander PretschnerProfessor of Computer Science, Technische Universität MünchenCorreu electrònic verificat a in.tum.de
