SPEEDY: An Eclipse-based IDE for invariant inference

SPEEDY is an Eclipse-based IDE for exploring techniques that assist users in generating correct specifications, particularly including invariant inference algorithms and tools. It integrates with several back-end tools that propose invariants and will incorporate published algorithms for inferring object and loop invariants. Though the architecture is language-neutral, current SPEEDY targets C programs. Building and using SPEEDY has confirmed earlier experience demonstrating the importance of showing and editing specifications in the IDEs that developers customarily use, automating as much of the production and checking of specifications as possible, and showing counterexample information directly in the source code editing environment. As in previous work, automation of specification checking is provided by back-end SMT solvers. However, reducing the effort demanded of software developers using formal methods also requires a GUI design that guides users in writing, reviewing, and correcting specifications and automates specification inference.Comment: In Proceedings F-IDE 2014, arXiv:1404.578

Invariant discovery and refinement plans for formal modelling in Event-B

The continuous growth of complex systems makes the development of correct software increasingly challenging. In order to address this challenge, formal methods o er rigorous mathematical techniques to model and verify the correctness of systems. Refinement is one of these techniques. By allowing a developer to incrementally introduce design details, refinement provides a powerful mechanism for mastering the complexities that arise when formally modelling systems. Here the focus is on a posit-and-prove style of refinement, where a design is developed as a series of abstract models introduced via refinement steps. Each refinement step generates proof obligations which must be discharged in order to verify its correctness – typically requiring a user to understand the relationship between modelling and reasoning. This thesis focuses on techniques to aid refinement-based formal modelling, specifically, when a user requires guidance in order to overcome a failed refinement step. An integrated approach has been followed: combining the complementary strengths of bottomup theory formation, in which theories about domains are built based on basic background information; and top-down planning, in which meta-level reasoning is used to guide the search for correct models. On the theory formation perspective, we developed a technique for the automatic discovery of invariants. Refinement requires the definition of properties, called invariants, which relate to the design. Formulating correct and meaningful invariants can be tedious and a challenging task. A heuristic approach to the automatic discovery of invariants has been developed building upon simulation, proof-failure analysis and automated theory formation. This approach exploits the close interplay between modelling and reasoning in order to provide systematic guidance in tailoring the search for invariants for a given model. On the planning perspective, we propose a new technique called refinement plans. Refinement plans provide a basis for automatically generating modelling guidance when a step fails but is close to a known pattern of refinement. This technique combines both modelling and reasoning knowledge, and, contrary to traditional pattern techniques, allow the analysis of failure and partial matching. Moreover, when the guidance is only partially instantiated, and it is suitable, refinement plans provide specialised knowledge to further tailor the theory formation process in an attempt to fully instantiate the guidance. We also report on a series of experiments undertaken in order to evaluate the approaches and on the implementation of both techniques into prototype tools. We believe the techniques presented here allow the developer to focus on design decisions rather than on analysing low-level proof failures

Automated conjecturing III : property-relations conjectures

Discovery in mathematics is a prototypical intelligent behavior, and an early and continuing goal of artificial intelligence research. We present a heuristic for producing mathematical conjectures of a certain typical form and demonstrate its utility. Our program conjectures relations that hold between properties of objects (property-relation conjectures). These objects can be of a wide variety of types. The statements are true for all objects known to the program, and are the simplest statements which are true of all these objects. The examples here include new conjectures for the hamiltonicity of a graph, a well-studied property of graphs. While our motivation and experiments have been to produce mathematical conjectures-and to contribute to mathematical research-other kinds of interesting property-relation conjectures can be imagined, and this research may be more generally applicable to the development of intelligent machinery

On the Notion of Interestingness in Automated Mathematical Discovery

Deciding whether something is interesting or not is of central importance in automated mathematical discovery, as it helps determine both the search space and search strategy for finding and evaluating concepts and conjectures

Ideas for a high-level proof strategy language

ABSTRACT Finding ways to prove theorems mechanically was one of the earliest challenges tackled by the AI community. Notable progress has been made but there is still always a limit to any set of heuristic search techniques. From a proof done by human users, we wish to find out whether AI techniques can also be used to learn from a human user. AI4FM (Artificial Intelligence for Formal Methods) is a four-year project that starts officially in April 2010 (see www.AI4FM.org). It focuses on helping users of "formal methods" many of which give rise to proof obligations that have to be (mechanically) verified (by a theorem prover). In industrial-sized developments, there are often a large number of proof obligations and, whilst many of them succumb to similar proof strategies, those that remain can hold up engineers trying to use formal methods. The goal of AI4FM is to learn enough from one manual proof, to discharge proof obligations automatically that yield to similar proof strategies. To achieve this, a high-level (proof) strategy language is required, and in this paper we outline some ideas of such language, and towards extracting them. * During this work Gudmund Grov has been employed jointly by University of Edinburgh and Newcastle University. and constrained use of Z [FW08] -is the so-called "posit and prove" approach: a designer posits development steps and then justifies that they satisfy earlier specifications by discharging (often automatically generated) proof obligations (POs). A large proportion of these POs can be discharged by automatic theorem provers but "some" proofs require user interaction. Quantifying "some" is hard since it depends on many factors such as the domain, technology and methodology used -it could be as little as 3% or as much as 40%. For example, the Paris Metro line 14, developed in the Bmethod, generated 27, 800 POs (of which around 2, 250 required user-interaction) [Abr07] -the need for interactive proofs is clearly still a bottleneck in industrial application of FM, notwithstanding high degree of automation. THE FORMAL METHODS PROBLE

TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the entangled geometric complexity and biological complexity. We introduce topology, i.e., element specific persistent homology (ESPH), to untangle geometric complexity and biological complexity. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains crucial biological information via a multichannel image representation. It is able to reveal hidden structure-function relationships in biomolecules. We further integrate ESPH and convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the limitations to deep learning arising from small and noisy training sets, we present a multitask topological convolutional neural network (MT-TCNN). We demonstrate that the present TopologyNet architectures outperform other state-of-the-art methods in the predictions of protein-ligand binding affinities, globular protein mutation impacts, and membrane protein mutation impacts.Comment: 20 pages, 8 figures, 5 table

A Survey of Symbolic Execution Techniques

Many security and software testing applications require checking whether certain properties of a program hold for any possible usage scenario. For instance, a tool for identifying software vulnerabilities may need to rule out the existence of any backdoor to bypass a program's authentication. One approach would be to test the program using different, possibly random inputs. As the backdoor may only be hit for very specific program workloads, automated exploration of the space of possible inputs is of the essence. Symbolic execution provides an elegant solution to the problem, by systematically exploring many possible execution paths at the same time without necessarily requiring concrete inputs. Rather than taking on fully specified input values, the technique abstractly represents them as symbols, resorting to constraint solvers to construct actual instances that would cause property violations. Symbolic execution has been incubated in dozens of tools developed over the last four decades, leading to major practical breakthroughs in a number of prominent software reliability applications. The goal of this survey is to provide an overview of the main ideas, challenges, and solutions developed in the area, distilling them for a broad audience. The present survey has been accepted for publication at ACM Computing Surveys. If you are considering citing this survey, we would appreciate if you could use the following BibTeX entry: http://goo.gl/Hf5FvcComment: This is the authors pre-print copy. If you are considering citing this survey, we would appreciate if you could use the following BibTeX entry: http://goo.gl/Hf5Fv

Mathematical applications of inductive logic programming

Accepted versio