Supporting the evolution of event-driven service-oriented architectures using change patterns

Abstract

ContextThe components of an event-driven service-oriented architecture (EDSOA) are composed in a highly decoupled way, facilitating high flexibility, scalability and concurrency in SOA systems. Evolving an EDSOA is challenging because the absence of explicit dependencies among constituent components makes understanding and analysing the overall system composition difficult. The evolution of EDSOAs typically happens by performing a series of primitive changes—which can be described formally as change primitives. ObjectiveIn this article, we present our change pattern based approach for managing the EDSOA evolution as a novel design method supporting EDSOA evolution. The change patterns operate on a higher abstraction level than change primitives. MethodTo evaluate our approach, we have compared both time and correctness of changes in a controlled experiment comparing the understanding and performing of changes in EDSOAs. The experiment has been conducted with 90 students of the Software Architecture course at the University of Vienna. We compare the efficiency of 3 sets of change operations for modifying a given system architecture to obtain a desired architecture: a minimal set of 3 change patterns, an extended set of 5 change patterns, and a minimal set of 4 change primitives. ResultsOur results show that change patterns based evolution requires significantly less time to capture a similar level of correctness as the evolution based on change primitives, presuming that a certain level of transformation complexity is required. Furthermore, we did not observe a significant difference in the correctness level nor in the time required to perform the changes using an extended pattern set compared to a minimal set of patterns. ConclusionsWe clearly show the feasibility of our approach by developing a design method and tool support using a model-driven tool chain consisting of 3 domain-specific languages and empirically evaluating the approach in a controlled experiment.