Software Engineering for Data Science

Software Engineering for Data Science

The SEEDS group is dedicated to the cross-fertilization of software engineering, data science and machine learning:

  • Software engineering for Data Science addresses the use of software engineering techniques for improving the ease of use, predictabilty, reliability and robustness of current data analytics and machine learning tools, languages and frameworks. Our SimpleML project, dedicated to the development of an easy to use domain-specific language for data analytics, exemplifies this direction.
  • Data Science for Software Engineering investigates how machine learning / data science can provide new solutions to classic software engineering problems such as software quality analysis, fault detection, program comprehension, human computer / computer human interaction, etc., advancing the way in which software is designed, synthetised and assessed.

Publications