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Bernhard Scholz
Researcher at University of Sydney
Publications - 108
Citations - 2099
Bernhard Scholz is an academic researcher from University of Sydney. The author has contributed to research in topics: Compiler & Datalog. The author has an hindex of 22, co-authored 101 publications receiving 1653 citations. Previous affiliations of Bernhard Scholz include Information Technology University & Vienna University of Technology.
Papers
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Journal ArticleDOI
MadMax: surviving out-of-gas conditions in Ethereum smart contracts
TL;DR: MadMax is presented: a static program analysis technique to automatically detect gas-focused vulnerabilities with very high confidence and achieves high precision and scalability.
Posted Content
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Lexi Brent,Anton Jurisevic,Michael Kong,Eric Liu,François Gauthier,Vincent Gramoli,Ralph Holz,Bernhard Scholz +7 more
TL;DR: Vandal is both fast and robust, successfully analysing over 95% of all 141k unique contracts with an average runtime of 4.15 seconds; outperforming the current state of the art tools---Oyente, EthIR, Mythril, and Rattle---under equivalent conditions.
Book ChapterDOI
Soufflé: On Synthesis of Program Analyzers
TL;DR: This tool paper introduces the Souffle architecture, usage and demonstrates its applicability for large-scale code analysis on the OpenJDK7 library as a use case.
Proceedings ArticleDOI
Gigahorse: thorough, declarative decompilation of smart contracts
TL;DR: Gigahorse offers a full-featured toolchain for further analyses (and a ``batteries included'' approach, with multiple clients already implemented), together with the highest performance and scalability, and uses a declarative, logic-based specification, which allows high-level insights to inform low-level decompilation.
Proceedings ArticleDOI
Register allocation for irregular architectures
Bernhard Scholz,Erik Eckstein +1 more
TL;DR: This work proposes a fundamentally new approach to global register allocation for irregular architectures that formulates global allocation as a partitioned boolean quadratic optimization problem (PBQP) that allows generic modeling of processors peculiarities.