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Model counting procedures for data structures are crucial for advancing the field of automated quantitative program analysis. We present a tool for Model Counting for Bounded Array Theory (MCBAT). MCBAT works on quantified integer array constraints in which all arrays have a finite length. We employ reductions from the theory of arrays to uninterpreted functions and linear integer arithmetic (LIA). Once reduced to LIA, we leverage Barvinok's polynomial time integer lattice point enumeration algorithm. Finally, we present a case study demonstrating applicability to automated quantitative program analysis. MCBAT is available for immediate use as a Docker image and the source code is freely available in our Github repository. CCS CONCEPTS • Software and its engineering → Formal methods; • Mathematics of computing → Combinatoric problems.

We present a method of automatically synthesizing steps to solve search problems. Given a specification of a search problem, our approach uses symbolic execution to analyze the specification in order to extract a set of constraints which model the problem. These constraints are used in a process called model counting, which is leveraged to compute probability distributions relating search steps to predicates about an unknown target. The probability distribution functions determine an information gain objective function based on Shannon entropy, which, when maximized, yields the next optimal step of the search. We prove that our algorithm converges to a correct solution, and discuss computational complexity issues. We implemented a domain specific language in which to write search problem specifications, enabling our static analysis phase. Our experiments demonstrate the effectiveness of our approach on a set of search problem case studies inspired by the domains of software security, computational geometry, AI for games, and user preference ranking.

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