Current software engineering practice has shown large interest and demonstrated quick adoption of experimentation ideas and practices,
and, more generally of data science platforms and techniques that will allow to generate value from users’ data by catering for their needs.
Additionally, the large amount of data collected at runtime, together with the increasing need to understand the impact changes on software
systems have on end users call for novel software engineering approaches that focus on experiment-driven feature development, data collection
pipelines and architectures, runtime frameworks and data management for data-driven decisions.
This is also manifested in the recent DataOps movement, which strives to streamline the development and operation of data analytics pipelines using agile practices.
To realize these approaches and develop innovative solutions with respect to automation and tooling for runtime optimization, decision making and data management,
there is a need for deep synergies between software engineers, data scientists and researchers.
In align with the overall goals of DDrEE on identifying problems in adoption and use of data-driven decisions,
discussing new ideas and innovative use cases, and building a community, DDrEE 2020 will focus is on open topics identified in the
of the previous edition
of the workshop:
Data collection, and usage
methods and tools for efficient data collection and usage
how to support and enhance online experimentation
Synergies between disciplines
synergies between data scientists and software engineers