Context-Oriented Behavioral Programming

Abstract

Modern systems require programmers to develop code that dynamically adapts to different contexts, leading to the evolution of new context-oriented programming languages. These languages introduce new software-engineering challenges, such as: how to maintain and keep the separation of concerns of the codebase? how to model the changing behaviors? how to verify the system behavior? and more. This paper introduces Context-Oriented Behavioral Programming(COBP) - a novel paradigm for developing context-aware systems, centered on natural and incremental specification of context-dependent behaviors. As the name suggests, we combine behavioral-programming(BP) - a scenario-based modeling paradigm - with context idioms that explicitly specify when scenarios are relevant and what information they need. The core idea is to connect the behavioral model with a data model that represents the context, allowing an intuitive connection between the models via update and select queries. Combining behavioral-programming with context-oriented programming brings the best of the two worlds, solving issues that arise when using each of the approaches in separation. We begin with providing abstract semantics for COBP, laying the foundations for applying reasoning algorithms to context-aware behavioral programs. We then exemplify the semantics with formal specifications of systems, including a variant of Conway's Game of Life. Finally, we present a JavaScript-based implementation of the paradigm and provide two case studies of real-life context-aware systems (one in robotics and another in IoT) that were developed using this tool. Throughout the examples and case studies, we provide design patterns and a methodology for coping with the above challenges.