Runtime verification for biochemical programs

The biochemical paradigm is well-suited for modelling autonomous systems and new programming languages are emerging from this approach. However, in order to validate such programs, we need to define precisely their semantics and to provide verification techniques. In this paper, we consider a higher-order biochemical calculus that models the structure of system states and its dynamics thanks to rewriting abstractions, namely rules and strategies. We extend this calculus with a runtime verification technique in order to perform automatic discovery of property satisfaction failure. The property specification language is a subclass of LTL safety and liveness properties

Interpreting Models of Social Group Interactions in Meetings with Probabilistic Model Checking

A major challenge in Computational Social Science consists in modelling and explaining the temporal dynamics of human communication. Understanding small group interactions can help shed light on sociological and social psychological questions relating to human communications. Previous work showed how Markov rewards models can be used to analyse group interaction in meeting. We explore further the potential of these models by formulating queries over interaction as probabilistic temporal logic properties and analysing them with probabilistic model checking. For this study, we analyse a dataset taken from a standard corpus of scenario and non-scenario meetings and demonstrate the expressiveness of our approach to validate expected interactions and identify patterns of interest

Temporal analytics for software usage models

We address the problem of analysing how users actually interact with software. Users are heterogeneous: they adopt different usage styles and each individual user may move between different styles, from one interaction session to another, or even during an interaction session. For analysis, we require new temporal analytics: techniques to model and analyse temporal data sets of logged interactions with the purpose of discovering, interpreting, and communicating meaningful patterns of usage. We define new probabilistic models whose parameters are inferred from logged time series data of user-software interactions. We formulate hypotheses about software usage together with the developers, encode them in probabilistic temporal logic, and analyse the models according to the probabilistic properties. We illustrate by application to logged data from a deployed mobile application software used by thousands of users

Data-driven modelling and probabilistic analysis of interactive software usage

This paper answers the research question: how can we model and understand the ways in which users actually interact with software, given that usage styles vary from user to user, and even from use to use for an individual user. Our first contribution is to introduce two new probabilistic, admixture models, inferred from sets of logged user traces, which include observed and latent states. The models encapsulate the temporal and stochastic aspects of usage, the heterogeneous and dynamic nature of users, and the temporal aspects of the time interval over which the data was collected (e.g. one day, one month, etc.). A key concept is activity patterns, which encapsulate common observed temporal behaviours shared across a set of logged user traces. Each activity pattern is a discrete-time Markov chain in which observed variables label the states; latent states specify the activity patterns. The second contribution is how we use parametrised, probabilistic, temporal logic properties to reason about hypothesised behaviours within an activity pattern, and between activity patterns. Different combinations of inferred model and hypothesised property afford a rich set of techniques for understanding software usage. The third contribution is a demonstration of the models and temporal logic properties by application to user traces from a software application that has been used by tens of thousands of users worldwide

PORGY: a Visual Analytics Platform for System Modelling and Analysis Based on Graph Rewriting

PORGY is a visual environment for rule-based modelling based on port graphs and port graph rewrite rules whose application is steered by rewriting strategies. The focus of this demonstration is the visual and interactive features offered by PORGY, which facilitate an exploratory approach to model, simu- late and analyse different ways of applying the rules while recording the model evolution, as well as tracking and plotting system parameters

A Practice Enquiry Design to Investigate How Pair Programming Can Help with Constructing Automata

Finite state automata (FSA) are a fundamental concept in the theory of computation and the undergraduate computer science education. However often students encounter difficulties with the task of constructing them due their abstract, theoretical nature. We present a disciplinary enquiry design investigating to what extent Pair Programming (PP) as a collaborative learning activity impacts on Software Engineering (SE) undergraduate students’ perceived and assessed performance when used for the task of constructing FSA

Chemical Rules and Term Rewriting

In this internship report we study interesting capabilities of TOM for modelling a particular class of molecular graphs and its associated graph rewriting relation by means of term rewriting. We also present a comparison between the design and the execution of the resulting implementation in TOM and those of GasEl, the ELAN implementation of this model

PORGY: Strategy-Driven Interactive Transformation of Graphs

This paper investigates the use of graph rewriting systems as a modelling tool, and advocates the embedding of such systems in an interactive environment. One important application domain is the modelling of biochemical systems, where states are represented by port graphs and the dynamics is driven by rules and strategies. A graph rewriting tool's capability to interactively explore the features of the rewriting system provides useful insights into possible behaviours of the model and its properties. We describe PORGY, a visual and interactive tool we have developed to model complex systems using port graphs and port graph rewrite rules guided by strategies, and to navigate in the derivation history. We demonstrate via examples some functionalities provided by PORGY.Comment: In Proceedings TERMGRAPH 2011, arXiv:1102.226

Balancing turn-based games with chained strategy generation

Probabilistic model checking can overcome much of the complexity inherent in balancing games. Game balancing is the careful maintenance of relationships between the ways in which a game can be played, to ensure no single way is strictly better than all others and that players are offered a wide variety of ways to play successfully. We introduce a novel approach towards automating game balancing using probabilistic model checking called chained strategy generation (CSG). This involves generating chains of adversarial strategies which mimic the way players adapt their approach during repeated plays of a game. We use CSG to map out the evolving metagame. The trends identified can allow game developers to identify strategies which will be too strong and ways of playing the game which a player may want to use, but are never viable for successful competitive play. We introduce a case study, a game called RPGLite, and use CSG to compare five candidate configurations for the game. We show how to determine which configurations of RPGLite lead to a more fair and interesting experience for players. We also identify unexpected trends in how the strategies evolve. Our approach introduces a new technique for improving game development and player experience