A grammatical specification of human-computer dialogue

The Seeheim Model of human-computer interaction partitions an interactive application into a user-interface, a dialogue controller and the application itself. One of the formal techniques of implementing the dialogue controller is based on context-free grammars and automata. In this work, we modify an off-the-shelf compiler generator (YACC) to generate the dialogue controller. The dialogue controller is then integrated into the popular X-window system, to create an interactive-application generator. The actions of the user drive the automaton, which in turn controls the application

FairFuzz: Targeting Rare Branches to Rapidly Increase Greybox Fuzz Testing Coverage

In recent years, fuzz testing has proven itself to be one of the most effective techniques for finding correctness bugs and security vulnerabilities in practice. One particular fuzz testing tool, American Fuzzy Lop or AFL, has become popular thanks to its ease-of-use and bug-finding power. However, AFL remains limited in the depth of program coverage it achieves, in particular because it does not consider which parts of program inputs should not be mutated in order to maintain deep program coverage. We propose an approach, FairFuzz, that helps alleviate this limitation in two key steps. First, FairFuzz automatically prioritizes inputs exercising rare parts of the program under test. Second, it automatically adjusts the mutation of inputs so that the mutated inputs are more likely to exercise these same rare parts of the program. We conduct evaluation on real-world programs against state-of-the-art versions of AFL, thoroughly repeating experiments to get good measures of variability. We find that on certain benchmarks FairFuzz shows significant coverage increases after 24 hours compared to state-of-the-art versions of AFL, while on others it achieves high program coverage at a significantly faster rate

Constructional Tools as the Origin of Cognitive Capacities

It is argued that cognitive capacities can be understood as the outcome of the collective action of a set of agents created by tools that explore possible behaviours and train the agents to behave in such appropriate ways as may be discovered. The coherence of the whole system is assured by a combination of vetting the performance of new agents and dealing appropriately with any faults that the whole system may develop. This picture is shown to account for a range of cognitive capacities, including language

Triple patterns: Compact specifications for the generation of operational triple graph grammar rules

Proceedings of the Sixth International Workshop on Graph Transformation and Visual Modeling Techniques (GT-VMT 2007)Triple Graph Grammars (TGGs) allow the specification of high-level rules modelling the synchronized creation of elements in two graphs related through a correspondence graph. Low-level operational rules are then derived to manipulate concrete graphs. However, TGG rules may become unnecessarily verbose when elements have to be replicated from one graph to the other, and their actual derivation cannot exploit the presence of reoccurring patterns. Moreover they do not take advantage from situations in which a normal creation grammar for one of the graphs exists, from which TGG operational rules can be derived to build the other graph. We present an approach to generating TGG operational rules from normal ones, reducing the information needed to derive them, through the definition of Triple Patterns, a high-level, compact, declarative, and visual notation for the description of admissible structures in a triple graph. Patterns can be expressed with respect to classes defined in a meta-model, and instantiated with derived classes at the model level, thus exploiting the inheritance hierarchies. The application of the generated rules results into the (synchronized or batch) creation of the structures specified in the patterns. We illustrate these concepts by showing their application to the synchronized incremental construction of visual models and of their semantics.This work has been partially sponsored by the Spanish Ministry of Education and Science with projects MOSAIC (TSI2005-08225-C07-06) and MODUWEB (TIN 2006-09678), and the EC’s Human Potential Programme under contract HPRN-CT-2002-00275, SegraVis. The authors gratefully thank the referees for their useful suggestion