Calysto: scalable and precise extended static checking
Proceedings of the 30th international conference on Software engineering
User-Friendly Model Checking: Automatically Configuring Algorithms with RuleBase/PE
HVC '08 Proceedings of the 4th International Haifa Verification Conference on Hardware and Software: Verification and Testing
Boolean satisfiability from theoretical hardness to practical success
Communications of the ACM - A Blind Person's Interaction with Technology
Beaver: Engineering an Efficient SMT Solver for Bit-Vector Arithmetic
CAV '09 Proceedings of the 21st International Conference on Computer Aided Verification
Instance-Based Selection of Policies for SAT Solvers
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
SATenstein: automatically building local search SAT solvers from components
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Improving GASAT by replacing tabu search by DLM and enhancing the best members
Artificial Intelligence Review
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Simulation vs. formal: absorb what is useful; reject what is useless
HVC'07 Proceedings of the 3rd international Haifa verification conference on Hardware and software: verification and testing
A gender-based genetic algorithm for the automatic configuration of algorithms
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
A modular CNF-based SAT solver
SBCCI '10 Proceedings of the 23rd symposium on Integrated circuits and system design
Domain Independent Goal Recognition
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Tradeoffs in the empirical evaluation of competing algorithm designs
Annals of Mathematics and Artificial Intelligence
Communications of the ACM
Dynamic scoring functions with variable expressions: new SLS methods for solving SAT
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
Automated configuration of mixed integer programming solvers
CPAIOR'10 Proceedings of the 7th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Sequential model-based optimization for general algorithm configuration
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
HAL: a framework for the automated analysis and design of high-performance algorithms
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Parallel algorithm configuration
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Quantifying homogeneity of instance sets for algorithm configuration
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Automatic (offline) configuration of algorithms
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Algorithm runtime prediction: Methods & evaluation
Artificial Intelligence
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Parameterized heuristics abound in computer aided design and verification, and manual tuning of the respective parameters is difficult and time-consuming. Very recent results from the artificial intelligence (AI) community suggest that this tuning process can be automated, and that doing so can lead to significant performance improvements; furthermore, automated parameter optimization can provide valuable guidance during the development of heuristic algorithms. In this paper, we study how such an AI approach can improve a state-of-the-art SAT solver for large, real-world bounded model-checking and software verification instances. The resulting, automatically-derived parameter settings yielded runtimes on average 4.5 times faster on bounded model checking instances and 500 times faster on software verification problems than extensive hand-tuning of the decision procedure. Furthermore, the availability of automatic tuning influenced the design of the solver, and the automatically-derived parameter settings provided a deeper insight into the properties of problem instances.