Daniel Kroening mainly focuses on Model checking, Programming language, Theoretical computer science, Software and Predicate abstraction. His Model checking study is concerned with the larger field of Algorithm. His study looks at the relationship between Programming language and fields such as Verilog, as well as how they intersect with chemical problems.
His Theoretical computer science study integrates concerns from other disciplines, such as ANSI C, Propositional calculus, Pointer and Symbolic execution. His Software research integrates issues from Machine learning, Correctness, Artificial intelligence and Deep neural networks. His studies in Predicate abstraction integrate themes in fields like Kernel, Boolean algebra, Reachability, Shared memory and Abstraction model checking.
His main research concerns Programming language, Theoretical computer science, Model checking, Software and Algorithm. His Programming language course of study focuses on Verilog and Formal equivalence checking. Theoretical computer science connects with themes related to Set in his study.
His research integrates issues of Discrete mathematics, Counterexample, Program analysis and Correctness in his study of Model checking. His work focuses on many connections between Software and other disciplines, such as Parallel computing, that overlap with his field of interest in Thread. He has included themes like Software verification and validation, Verification, Software engineering and Embedded software in his Software verification study.
His scientific interests lie mostly in Software, Programming language, Artificial intelligence, Theoretical computer science and Deep neural networks. The concepts of his Software study are interwoven with issues in Java and Software engineering. Daniel Kroening regularly links together related areas like Exploit in his Programming language studies.
His studies examine the connections between Artificial intelligence and genetics, as well as such issues in Machine learning, with regards to Range, Benchmark and Set. His Theoretical computer science study frequently links to adjacent areas such as Modular design. Daniel Kroening works mostly in the field of Model checking, limiting it down to topics relating to Software verification and, in certain cases, Embedded software.
The scientist’s investigation covers issues in Deep neural networks, Reinforcement learning, Mathematical optimization, Artificial intelligence and Limit. His research on Deep neural networks also deals with topics like
His Machine learning research includes elements of Software and Code coverage. Java bytecode is connected with Set and Programming language in his research. Daniel Kroening interconnects Data center and Code in the investigation of issues within Programming language.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
A Tool for Checking ANSI-C Programs
Edmund M. Clarke;Daniel Kroening;Flavio Lerda.
tools and algorithms for construction and analysis of systems (2004)
A Survey of Automated Techniques for Formal Software Verification
V. D'Silva;D. Kroening;G. Weissenbacher.
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2008)
Behavioral consistency of C and Verilog programs using bounded model checking
Edmund Clarke;Daniel Kroening;Karen Yorav.
design automation conference (2003)
Satabs : SAT-based predicate abstraction for ANSI-C
Edmund Clarke;Daniel Kroening;Natasha Sharygina;Karen Yorav.
Lecture Notes in Computer Science (2005)
Decision Procedures: An Algorithmic Point of View
Daniel Kroening;Ofer Strichman.
(2008)
CBMC – C Bounded Model Checker
Daniel Kroening;Michael Tautschnig.
tools and algorithms for construction and analysis of systems (2014)
Efficient computation of recurrence diameters
Daniel Kroening;Ofer Strichman.
Lecture Notes in Computer Science (2003)
Predicate Abstraction of ANSI-C Programs Using SAT
Edmund Clarke;Daniel Kroening;Natasha Sharygina;Karen Yorav.
formal methods (2004)
Concolic testing for deep neural networks
Youcheng Sun;Min Wu;Wenjie Ruan;Xiaowei Huang.
automated software engineering (2018)
Error explanation with distance metrics
Alex Groce;Sagar Chaki;Daniel Kroening;Ofer Strichman.
International Journal on Software Tools for Technology Transfer (2006)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:
Carnegie Mellon University
Technion – Israel Institute of Technology
University of Oxford
Amazon (United States)
Max Planck Institute for Software Systems
Northern Arizona University
University of California, Berkeley
University of Freiburg
Fondazione Bruno Kessler
University of Oxford
Carnegie Mellon University
University of California, Irvine
Chinese Academy of Sciences
University of Twente
University of Bremen
University of Western Ontario
Harbin Institute of Technology
National Institutes of Health
Osaka University
University of California, Irvine
University of Ulm
ETH Zurich
University of Montreal
National Institutes of Health
University of Oslo
University of Montreal