Software Engineering & Technology (SET)
Software evolves and continuously grows in:
- size in terms of lines of code, methods, classes, modules
- provided features
- costs to develop, and
- languages used
Analysis of data originating from software models, systems and ecosystems and translation of the insights obtained into industrial applications. In particular, we are interested in:
- how developers create and maintain systems and their models
- how developers communicate and collaborate
- and what hinders or facilitates those processes
Answering those questions requires application of data science techniques to software engineering data. We build publicly available tools and provide data supporting further research!
The group closely collaborates with major high-tech companies and research institutions world-wide. An example of such a successful collaboration was the CRYSTAL projects with 72 partners from all over Europe. We have discovered that bottle-necks in the bug resolution process were related to organizational issues rather than engineering challenges.
In collaboration with University of California, Davis, USA, we have analyzed 23,000 open source projects, studied various kinds of diversity and established that gender diversity is a positive predictor for productivity. This suggests that added investments in professional training efforts and outreach for female programmers is likely to result in added overall value.
- MLSAVE-A (NWO)Data-science research of social aspects of collaboration in online software development communities.
- MMP (EU FP7)As part of the study of smart design of nano-enabled products, data science techniques have been applied to study large collection of meta-models.
- CPS (Company-funded)Using data science techniques we study evolution of meta-models in a large industrial ecosystem.