Formal approaches to software analysis and development tend to focus on greenfield scenarios or to look at some piece of given software as a static object. Dynamic evolution of software is a much more common and relevant issue, and its importance keeps growing. Key drivers are: (i) The advent of innovative execution platforms, including massively parallel and re-configurable hardware; (ii) emerging norms and regulations demanding software being re-engineered to comply with stricter legislation, new quality requirements, or ethical standards; (iii) the role of software in science ("in silico" now surpasses the more traditional "in vitro" / "in vivo" research methods); (iv) novel application scenarios for existing software (blockchain, micro-services, IoT, etc.), fueled by the digitalization of everything; (v) the growing importance of simulations as a tool to model, understand, and predict complex, dynamic behavior, specifically with a feedback loop to obtain a digital twin. Software refactoring, parallelization, and adaptation have become central activities in the value chain: Automating them can realize huge gains. Formal approaches to software modeling and analysis are poised to make a substantial contribution, because they are fundamentally concerned with automation and correctness. We invited researchers with an active interest in the automation of software re-engineering: People working on formal foundations, on tools, as well as practitioners of re-engineering. A special focus was to look at re-engineering of simulation software, digital twins, and of applications in IoT scenarios.
