Context-Oriented Programming: A Programming Paradigm for Autonomic Systems

TL;DR

This paper introduces context-oriented programming languages as a novel paradigm to facilitate dynamic software adaptation in autonomic systems, emphasizing their integration within the MAPE-K loop and comparing them to existing approaches.

Contribution

It presents a new programming paradigm and framework for context-oriented programming, demonstrating its advantages over other dynamic adaptation techniques through a case study.

Findings

Context-oriented programming languages naturally support dynamic adaptation.

The proposed framework effectively integrates with the MAPE-K loop.

Case study shows improved flexibility in adaptive systems.

Abstract

Dynamic software adaptability is one of the central features leveraged by autonomic computing. However, developing software that changes its behavior at run time adapting to the operational conditions is a challenging task. Several approaches have been proposed in the literature to attack this problem at different and complementary abstraction levels: software architecture, middleware, and programming level. We focus on the support that ad-hoc programming language constructs may provide to support dynamically adaptive behaviors. We introduce context-oriented programming languages and we present a framework that positions the supported paradigm in the MAPE-K autonomic loop. We discuss the advantages of using context-oriented programming languages instead of other mainstream approaches based on dynamic aspect oriented programming languages and present a case study that shows how the proposed programming style naturally fits dynamic adaptation requirements. Finally, we discuss some known problems and outline a number of open research challenges.