Portfolio item number 1
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Posts by Collection
portfolio
Portfolio item number 2
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publications
Trau: SMT Solver for String Consraints
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
Paper Title Number 2
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Paper Title Number 3
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
talks
Counter-Example Guided Program Verification
Published:
This paper presents a novel counter-example guided abstraction refinement algorithm for the automatic verification of concurrent programs. Our algorithm proceeds in different steps. It first constructs an abstraction of the original program by slicing away a given subset of variables. Then, it uses an external model checker as a backend tool to analyze the correctness of the abstract program. If the model checker returns that the abstract program is safe then we conclude that the original one is also safe. If the abstract program is unsafe, we extract an abstract counter-example. In order to check if the abstract counter-example can lead to a real counter-example of the original program, we add back to the abstract counter-example all the omitted variables (that have been sliced away) to obtain a new program. Then, we call recursively our algorithm on the new obtained program. If the recursive call of our algorithm returns that the new program is unsafe, then we can conclude that the original program is also unsafe and our algorithm terminates. Otherwise, we refine the abstract program by removing the abstract counter-example from its set of possible runs. Finally, we repeat the procedure with the refined abstract program. We have implemented our algorithm, and run it successfully on the concurrency benchmarks in SV-COMP15. Our experimental results show that our algorithm significantly improves the performance of the backend tool.
Flatten and Conquer: A Framework for Efficient Analysis of String Constraints
Published:
We describe a uniform and efficient framework for checking the satisfiability of a large class of string constraints. The framework is based on the observation that both satisfiability and unsatisfiability of common constraints can be demonstrated through witnesses with simple patterns. These patterns are captured using flat-automata each of which consists of a sequence of simple loops. We build a Counter-Example Guided Abstraction Refinement (CEGAR) framework which contains both an under- and an over-approximation module. The flow of information between the modules allows to increase the precision in an automatic manner. We have implemented the framework as a tool and performed extensive experimentation that demonstrates both the generality and efficiency of our method.
A Framework for Efficient Analysis of String Constraints
Published:
Checking the satisfiability of string constraints is a crucial problem. It has been motivated by numerous application areas such as security, web programming and model checking. For example, cross-site scripting, one of the most common web vulnerabilities, is typically caused by improper handling of strings by web applications. However, existing string solvers are able to handle only fragments of string constraints because they come in very different forms.
Efficient Handling of String-Number Conversion
Published:
String-number conversion is an important class of constraints needed for the symbolic execution of string-manipulating programs. In particular solving string constraints with string-number conversion is necessary for the analysis of scripting languages such as JavaScript and Python, where string-number conversion is a part of the definition of the core semantics of these languages. However, solving this type of constraint is very challenging for the state-of-the-art solvers. We propose in this paper an approach that can efficiently support both string-number conversion and other common types of string constraints. Experimental results show that it significantly outperforms other state-of-the-art tools on benchmarks that involves string-number conversion.
teaching
Teaching experience 1
Undergraduate course, , 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.