Scaling-Up Behavioral Programming: Steps from Basic Principles to Application Architectures

Abstract

Behavioral programming (BP) is a decentralized scenario-based paradigm for the programming of reactive software, geared towards incremental and intuitive development. In this work we apply the principles of BP to a large, real-world case-study: a web-server. We discuss the conclusions learned from our attempt and propose several extension idioms to BP, aimed at improving the framework's scalability. Specifically, we propose extending BP with a timeout idiom for handling various time constraints, program-specific execution strategies, dynamic thread creation for efficiently allocating system resources, and support for parameterized events to handle inputs with infinite domains. Our extensions and case-study are implemented in a new framework for behavioral programming in C++.