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Software systems are at the heart of the digital society: They control critical infrastructures like communication or energy systems, fuel the increasing automation in industrial manufacturing and are key drivers of the digital economy. Despite this importance, the development of complex software systems is still a fundamental challenge. Credible reports indicate that the majority of software projects run over time or budget -- or fail altogether, resulting in billions of dollars wasted every year. And while technical aspects like, e.g., programming techniques, testing methods, or developer support tools have improved significantly over the past years, our understanding how human and social factors contribute to success or failure of software projects is still in its infancy.
Addressing these challenges, I use data science to quantitatively study collaborative software engineering processes. As an example, we use network analysis and statistical modeling to study the evolution of software architectures based on large-scale data from software repositories. This not only allows us to trace the maintainability of software systems. We can also assist developers in the refactoring of code. We further extract large data sets from online support tools, and analyze them to better understand how social factors influence software development processes. This approach has helped us to uncover social mechanisms at work in software development, to quantify risks in Open Source communities, and to improve information systems used by software development teams.
