Improving Runtime Overheads for detectEr

We design monitor optimisations for detectEr, a runtime-verification tool synthesising systems of concurrent monitors from correctness properties for Erlang programs. We implement these optimisations as part of the existing tool and show that they yield considerably lower runtime overheads when compared to the unoptimised monitor synthesis.Comment: In Proceedings FESCA 2015, arXiv:1503.0437

Combining Voting Rules Together

We propose a simple method for combining together voting rules that performs a run-off between the different winners of each voting rule. We prove that this combinator has several good properties. For instance, even if just one of the base voting rules has a desirable property like Condorcet consistency, the combination inherits this property. In addition, we prove that combining voting rules together in this way can make finding a manipulation more computationally difficult. Finally, we study the impact of this combinator on approximation methods that find close to optimal manipulations

Assigning Satisfaction Values to Constraints: An Algorithm to Solve Dynamic Meta-Constraints

The model of Dynamic Meta-Constraints has special activity constraints which can activate other constraints. It also has meta-constraints which range over other constraints. An algorithm is presented in which constraints can be assigned one of five different satisfaction values, which leads to the assignment of domain values to the variables in the CSP. An outline of the model and the algorithm is presented, followed by some initial results for two problems: a simple classic CSP and the Car Configuration Problem. The algorithm is shown to perform few backtracks per solution, but to have overheads in the form of historical records required for the implementation of state.Comment: 11 pages. Proceedings ERCIM WG on Constraints (Prague, June 2001

Regular Combinators for String Transformations

We focus on (partial) functions that map input strings to a monoid such as the set of integers with addition and the set of output strings with concatenation. The notion of regularity for such functions has been defined using two-way finite-state transducers, (one-way) cost register automata, and MSO-definable graph transformations. In this paper, we give an algebraic and machine-independent characterization of this class analogous to the definition of regular languages by regular expressions. When the monoid is commutative, we prove that every regular function can be constructed from constant functions using the combinators of choice, split sum, and iterated sum, that are analogs of union, concatenation, and Kleene-*, respectively, but enforce unique (or unambiguous) parsing. Our main result is for the general case of non-commutative monoids, which is of particular interest for capturing regular string-to-string transformations for document processing. We prove that the following additional combinators suffice for constructing all regular functions: (1) the left-additive versions of split sum and iterated sum, which allow transformations such as string reversal; (2) sum of functions, which allows transformations such as copying of strings; and (3) function composition, or alternatively, a new concept of chained sum, which allows output values from adjacent blocks to mix.Comment: This is the full version, with omitted proofs and constructions, of the conference paper currently in submissio

A Graphical Language for Proof Strategies

Complex automated proof strategies are often difficult to extract, visualise, modify, and debug. Traditional tactic languages, often based on stack-based goal propagation, make it easy to write proofs that obscure the flow of goals between tactics and are fragile to minor changes in input, proof structure or changes to tactics themselves. Here, we address this by introducing a graphical language called PSGraph for writing proof strategies. Strategies are constructed visually by "wiring together" collections of tactics and evaluated by propagating goal nodes through the diagram via graph rewriting. Tactic nodes can have many output wires, and use a filtering procedure based on goal-types (predicates describing the features of a goal) to decide where best to send newly-generated sub-goals. In addition to making the flow of goal information explicit, the graphical language can fulfil the role of many tacticals using visual idioms like branching, merging, and feedback loops. We argue that this language enables development of more robust proof strategies and provide several examples, along with a prototype implementation in Isabelle

JooFlux: Hijacking Java 7 InvokeDynamic To Support Live Code Modifications

Changing functional and non-functional software implementation at runtime is useful and even sometimes critical both in development and production environments. JooFlux is a JVM agent that allows both the dynamic replacement of method implementations and the application of aspect advices. It works by doing bytecode transformation to take advantage of the new invokedynamic instruction added in Java SE 7 to help implementing dynamic languages for the JVM. JooFlux can be managed using a JMX agent so as to operate dynamic modifications at runtime, without resorting to a dedicated domain-specific language. We compared JooFlux with existing AOP platforms and dynamic languages. Results demonstrate that JooFlux performances are close to the Java ones --- with most of the time a marginal overhead, and sometimes a gain --- where AOP platforms and dynamic languages present significant overheads. This paves the way for interesting future evolutions and applications of JooFlux