Lay summary
Enterprise software systems integrate many different components ranging from applications to legacy systems, middleware components or platforms, and to services. A managed evolution of such systems require appropriate abstraction models, continuous validation and monitoring of quality properties of components and services, as well as analysis techniques that investigate all sorts of accumulated data. Software systems exhibit a wealth of rich data to be analyzed for assessing and guiding their evolution: it ranges from requirements to architectural and design abstractions, the code base and its modification and change repositories, bug and issue tracking data, test data and test suites, execution trace data, and logs of the communication between different components. Since such data pervades requirements, architecture, design, implementation, configuration, testing, and deployment, we need to devise methods to deal with that allotrope.In this research module, we will focus on models, methods and technologies to manage the large-scale evolution of enterprise computing systems. The goal is to mine and integrate different static and dynamic data sources, build bridges between the different levels of abstraction from architecture and design to code and tests, and to develop means for effective enterprise software evolution support. We will focus on cross-model abstractions for requirements, analysis, and test evolution. The challenge will be to develop a cross-model (from architecture to code and test) and cross-role (for architects, developers or testers) integration to enable proactive steering of evolution both for static and dynamic system aspects. We will explore enterprise computing systems from a software evolution perspective and focus on models for managed evolution, their linkage across different abstraction levels (ranging from requirements to architecture, design, code and tests) as well as on the discovery of large-scale system evolution (anti-)patterns. Methods and technologies will be devised data analysis, evolution analysis and requirements evolution.