Proof-Pattern Recognition and Lemma Discovery in ACL2

We present a novel technique for combining statistical machine learning for proof-pattern recognition with symbolic methods for lemma discovery. The resulting tool, ACL2(ml), gathers proof statistics and uses statistical pattern-recognition to pre-processes data from libraries, and then suggests auxiliary lemmas in new proofs by analogy with already seen examples. This paper presents the implementation of ACL2(ml) alongside theoretical descriptions of the proof-pattern recognition and lemma discovery methods involved in it

Symbolic execution proofs for higher order store programs

Higher order store programs are programs which store, manipulate and invoke code at runtime. Important examples of higher order store programs include operating system kernels which dynamically load and unload kernel modules. Yet conventional Hoare logics, which provide no means of representing changes to code at runtime, are not applicable to such programs. Recently, however, new logics using nested Hoare triples have addressed this shortcoming. In this paper we describe, from top to bottom, a sound semi-automated verification system for higher order store programs. We give a programming language with higher order store features, define an assertion language with nested triples for specifying such programs, and provide reasoning rules for proving programs correct. We then present in full our algorithms for automatically constructing correctness proofs. In contrast to earlier work, the language also includes ordinary (fixed) procedures and mutable local variables, making it easy to model programs which perform dynamic loading and other higher order store operations. We give an operational semantics for programs and a step-indexed interpretation of assertions, and use these to show soundness of our reasoning rules, which include a deep frame rule which allows more modular proofs. Our automated reasoning algorithms include a scheme for separation logic based symbolic execution of programs, and automated provers for solving various kinds of entailment problems. The latter are presented in the form of sets of derived proof rules which are constrained enough to be read as a proof search algorithm

An automata-based automatic verification environment

With the continuing growth of computer systems including safety-critical computer control systems, the need for reliable tools to help construct, analyze, and verify such systems also continues to grow. The basic motivation of this work is to build such a formal verification environment for computer-based systems. An example of such a tool is the Design Oriented Verification and Evaluation (DOVE) created by Australian Defense Science and Technology Organization. One of the advantages of DOVE is that it combines ease of use provided by a graphical user interface for describing specifications in the form of extended state machines with the rigor of proving linear temporal logic properties in a robust theorem prover, Isabelle which was developed at Cambridge University, UK, and TU Munich, Germany. A different class of examples is that of model checkers, such as SPIN and SMV. In this work, we describe our technique to increase the utility of DOVE by extending it with the capability to build systems by specifying components. This added utility is demonstrated with a concrete example from a real project to study aspects of the control unit for an infusion pump being built at the Walter Reid Army Institute of Research. Secondly, we provide a formulation of linear temporal logic (LTL) in the theorem prover Isabelle. Next, we present a formalization of a variation of the algorithm for translating LTL into Büchi automata. The original translation algorithm is presented in Gerth et al and is the basis of model checkers such as SPIN. We also provide a formal proof of the termination and correctness of this algorithm. All definitions and proofs have been done fully formally within the generic theorem prover Isabelle, which guarantees the rigor of our work and the reliability of the results obtained. Finally, we introduce the automata theoretic framework for automatic verification as our future works