Learning from Big Code: Probabilistic Models, Analysis and Synthesis

Project Information

BIGCODE

Grant agreement ID: 680358

DOI

10.3030/680358

Project closed

EC signature date 18 February 2016

Start date 1 April 2016

End date 31 March 2021

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 500 000,00

EU contribution

€ 1 500 000,00

Coordinated by

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
Switzerland

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Publications

Conference proceedings (16)

Adversarial Robustness for Code

Author(s): Pavol Bielik, Martin Vechev
Published in: Proceedings of The 37rd International Conference on Machine Learning, 2020
Publisher: ICML

Learning to find naming issues with big code and small supervision

Author(s): Jingxuan He, Cheng-Chun Lee, Veselin Raychev, Martin Vechev
Published in: Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, 2021, Page(s) 296-311, ISBN 9781450383912
Publisher: ACM
DOI: 10.1145/3453483.3454045

Robustness certification with generative models

Author(s): Matthew Mirman, Alexander Hägele, Pavol Bielik, Timon Gehr, Martin Vechev
Published in: Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, 2021, Page(s) 1141-1154, ISBN 9781450383912
Publisher: ACM
DOI: 10.1145/3453483.3454100

Probabilistic model for code with decision trees

Author(s): Veselin Raychev, Pavol Bielik, Martin Vechev
Published in: Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications - OOPSLA 2016, 2016, Page(s) 731-747, ISBN 9781-450344449
Publisher: ACM Press
DOI: 10.1145/2983990.2984041

Learning programs from noisy data

Author(s): Veselin Raychev, Pavol Bielik, Martin Vechev, Andreas Krause
Published in: Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages - POPL 2016, 2016, Page(s) 761-774, ISBN 9781-450335492
Publisher: ACM Press
DOI: 10.1145/2837614.2837671

PHOG: Probabilistic Model for Code

Author(s): Pavol Bielik, Veselin Raychev, Martin Vechev
Published in: Proceedings of The 33rd International Conference on Machine Learning, 2016, Page(s) 2933-2942
Publisher: PMLR

Learning a Static Analyzer from Data

Author(s): Pavol Bielik, Veselin Raychev, Martin Vechev
Published in: Computer Aided Verification, 2017, Page(s) 233-253
Publisher: Springer International Publishing
DOI: 10.1007/978-3-319-63387-9_12

Statistical Deobfuscation of Android Applications

Author(s): Benjamin Bichsel, Veselin Raychev, Petar Tsankov, Martin Vechev
Published in: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security - CCS'16, 2016, Page(s) 343-355, ISBN 9781-450341394
Publisher: ACM Press
DOI: 10.1145/2976749.2978422

Debin - Predicting Debug Information in Stripped Binaries

Author(s): Jingxuan He, Pesho Ivanov, Petar Tsankov, Veselin Raychev, Martin Vechev
Published in: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security - CCS '18, 2018, Page(s) 1667-1680, ISBN 9781-450356930
Publisher: ACM Press
DOI: 10.1145/3243734.3243866

Learning to Solve SMT formulas

Author(s): Mislav Balunovic, Pavol Bielik, Martin Vechev
Published in: NeurIPS 2018, 2018
Publisher: Neurips

Differentiable Abstract Interpretation for Provably Robust Neural Networks

Author(s): MATTHEW MIRMAN, TIMON GEHR, MARTIN VECHEV
Published in: Proceedings of the 35 th International Conference on Machine Learning, 2018
Publisher: PMLR

AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation

Author(s): Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, Martin Vechev
Published in: 2018 IEEE Symposium on Security and Privacy (SP), 2018, Page(s) 3-18, ISBN 978-1-5386-4353-2
Publisher: IEEE
DOI: 10.1109/sp.2018.00058

Program Synthesis for Character Level Language Modeling

Author(s): PAVOL BIELIK, VESELIN RAYCHEV, MARTIN VECHEV
Published in: International Conference on Learning Representations, 2017
Publisher: ICLR

Inferring crypto API rules from code changes

Author(s): Rumen Paletov, Petar Tsankov, Veselin Raychev, Martin Vechev
Published in: Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation - PLDI 2018, 2018, Page(s) 450-464, ISBN 9781-450356985
Publisher: ACM Press
DOI: 10.1145/3192366.3192403

Learning fast and precise numerical analysis

Author(s): Jingxuan He, Gagandeep Singh, Markus Püschel, Martin Vechev
Published in: Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation, 2020, Page(s) 1112-1127, ISBN 9781450376136
Publisher: ACM
DOI: 10.1145/3385412.3386016

Adversarial Robustness for Code

Author(s): Bielik, Pavol; Vechev, Martin
Published in: Proceedings of Machine Learning Research, 119, Issue 6, 2020
Publisher: ACM ICML
DOI: 10.3929/ethz-b-000466229

Peer reviewed articles (1)

Robust relational layout synthesis from examples for Android

Author(s): Pavol Bielik, Marc Fischer, Martin Vechev
Published in: Proceedings of the ACM on Programming Languages, Issue 2/OOPSLA, 2018, Page(s) 1-29, ISSN 2475-1421
Publisher: ACM
DOI: 10.1145/3276526