The Configurable SAT Solver Challenge (CSSC)

It is well known that different solution strategies work well for different types of instances of hard combinatorial problems. As a consequence, most solvers for the propositional satisfiability problem (SAT) expose parameters that allow them to be customized to a particular family of instances. In the international SAT competition series, these parameters are ignored: solvers are run using a single default parameter setting (supplied by the authors) for all benchmark instances in a given track. While this competition format rewards solvers with robust default settings, it does not reflect the situation faced by a practitioner who only cares about performance on one particular application and can invest some time into tuning solver parameters for this application. The new Configurable SAT Solver Competition (CSSC) compares solvers in this latter setting, scoring each solver by the performance it achieved after a fully automated configuration step. This article describes the CSSC in more detail, and reports the results obtained in its two instantiations so far, CSSC 2013 and 2014

Using a SAT solver to generate checking sequences

Methods for software testing based on Finite State Machines (FSMs) have been researched since the early 60’s. Many of these methods are about generating a checking sequence from a given FSM which is an input sequence that determines whether an implementation of the FSM is faulty or correct. In this paper, we consider one of these methods, which constructs a checking sequence by reducing the problem of generating a checking sequence to finding a Chinese rural postman tour on a graph induced by the FSM; we re-formulate the constraints used in this method as a set of Boolean formulas; and use a SAT solver to generate a checking sequence of minimal length

EvoAlloy: An Evolutionary Approach For Analyzing Alloy Specifications

Using mathematical notations and logical reasoning, formal methods precisely define a program’s specifications, from which we can instantiate valid instances of a system. With these techniques, we can perform a variety of analysis tasks to verify system dependability and rigorously prove the correctness of system properties. While there exist well-designed automated verification tools including ones considered lightweight, they still lack a strong adoption in practice. The essence of the problem is that when applied to large real world applications, they are not scalable and applicable due to the expense of thorough verification process. In this thesis, I present a new approach and demonstrate how to relax the completeness guarantee without much loss, since soundness is maintained. I have extended a widely applied lightweight analysis, Alloy, with a genetic algorithm. Our new tool, EvoAlloy, works at the level of finite relations generated by Kodkod and evolves the chromosomes based on the feedback including failed constraints. Through a feasibility study, I prove that my approach can successfully find solutions to a set of specifications beyond the scope where traditional Alloy Analyzer fails. While EvoAlloy solves small size problems with longer time, its scalability provided by genetic extension shows its potential to handle larger specifications. My future vision is that when specifications are small I can maintain both soundness and completeness, but when this fails, EvoAlloy can switch to its genetic algorithm. Adviser: Hamid Bagher