Systems we build are ultimately evaluated based on the value they deliver to their users and stakeholders.
To increase the value, systems are subject to fast-paced evolution of the systems, due to unpredictable markets,
complex and changing customer requirements, pressures of shorter time-to-market, and rapidly advancing information technologies.
To address this situation, agile practices advocate flexibility, efficiency and speed.
Continuous software engineering refers to the organisational capability to develop,
release and learn from software in rapid parallel cycles, typically hours, days or very small numbers of weeks.
This includes to determine new functionality to build, evolving and refactoring the architecture, developing the functionality,
validating it, and releasing it to customers. One needs to relate the changes performed on the system with their effect
on the metrics of interest, keep the changes with positive effects, and discard the rest. In case of complex systems
involving humans in the loop, such a relation is difficult to infer a priori; a solution is then to observe and experiment
with systems in production environments, e.g. with continuous experimentation.
Reaching this goal requires crosscutting research which spans from the area of process and organisational aspects
in software engineering to the individual phases of the software engineering lifecycle and finally to live experimentation
to evaluate different system alternatives by users’ feedback. With the proliferation of data analysis and machine learning
techniques and flexible approaches to rapid deployment, experimentation can be used in different domains (e.g. embedded systems);
it can also be automated and used for runtime adaptation. These new concepts call for synergy between software engineers and data scientists.
RCoSE/DDrEE'19 brings together academics and practitioners with the overall goals:
to identify the problems in adoption and use of continuous software engineering and data-driven decisions
to discuss new ideas that apply successfull and established concepts to other domains and use cases
to build a community between software engineers and data scientists working on a common research agenda