Predicting SMT solver performance for software verification

The approach Why3 takes to interfacing with a wide variety of interactive and automatic theorem provers works well: it is designed to overcome limitations on what can be proved by a system which relies on a single tightly-integrated solver. In common with other systems, however, the degree to which proof obligations (or “goals”) are proved depends as much on the SMT solver as the properties of the goal itself. In this work, we present a method to use syntactic analysis to characterise goals and predict the most appropriate solver via machine-learning techniques. Combining solvers in this way - a portfolio-solving approach - maximises the number of goals which can be proved. The driver-based architecture of Why3 presents a unique opportunity to use a portfolio of SMT solvers for software verification. The intelligent scheduling of solvers minimises the time it takes to prove these goals by avoiding solvers which return Timeout and Unknown responses. We assess the suitability of a number of machinelearning algorithms for this scheduling task. The performance of our tool Where4 is evaluated on a dataset of proof obligations. We compare Where4 to a range of SMT solvers and theoretical scheduling strategies. We find that Where4 can out-perform individual solvers by proving a greater number of goals in a shorter average time. Furthermore, Where4 can integrate into a Why3 user’s normal workflow - simplifying and automating the non-expert use of SMT solvers for software verification

Predicting SMT solver performance for software verification

The approach Why3 takes to interfacing with a wide variety of interactive and automatic theorem provers works well: it is designed to overcome limitations on what can be proved by a system which relies on a single tightly-integrated solver. In common with other systems, however, the degree to which proof obligations (or “goals”) are proved depends as much on the SMT solver as the properties of the goal itself. In this work, we present a method to use syntactic analysis to characterise goals and predict the most appropriate solver via machine-learning techniques. Combining solvers in this way - a portfolio-solving approach - maximises the number of goals which can be proved. The driver-based architecture of Why3 presents a unique opportunity to use a portfolio of SMT solvers for software verification. The intelligent scheduling of solvers minimises the time it takes to prove these goals by avoiding solvers which return Timeout and Unknown responses. We assess the suitability of a number of machinelearning algorithms for this scheduling task. The performance of our tool Where4 is evaluated on a dataset of proof obligations. We compare Where4 to a range of SMT solvers and theoretical scheduling strategies. We find that Where4 can out-perform individual solvers by proving a greater number of goals in a shorter average time. Furthermore, Where4 can integrate into a Why3 user’s normal workflow - simplifying and automating the non-expert use of SMT solvers for software verification

The limits of human predictions of recidivism.

Dressel and Farid recently found that laypeople were as accurate as statistical algorithms in predicting whether a defendant would reoffend, casting doubt on the value of risk assessment tools in the criminal justice system. We report the results of a replication and extension of Dressel and Farid's experiment. Under conditions similar to the original study, we found nearly identical results, with humans and algorithms performing comparably. However, algorithms beat humans in the three other datasets we examined. The performance gap between humans and algorithms was particularly pronounced when, in a departure from the original study, participants were not provided with immediate feedback on the accuracy of their responses. Algorithms also outperformed humans when the information provided for predictions included an enriched (versus restricted) set of risk factors. These results suggest that algorithms can outperform human predictions of recidivism in ecologically valid settings

Large Area Crop Inventory Experiment (LACIE). Experiment plan for evaluation of LANDSAT agronomic variables using wheat intensive test sites

There are no author-identified significant results in this report

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