How Do Developers Blog?
Mining Software Repositories

How Do Developers Blog?

Our work on data-mining blogs and related artifacts in open source communities received the MSR'21 Most Influential Paper Award. Time to reflect.

  • Date: 01 Jan 2022

A decade ago, the rise of GitHub and StackOverflow as social version control and knowledge sharing environments was about to start. Social media like Twitter were mocked by some software engineering researchers and practitioners as “tools for kids not professionals”. At that time, we published one of the first papers on social media in software engineering at MSR 2011, the Mining Software Repositories Conference.

Until then, the MSR community focused primarily on mining source code from version control systems, e.g., for defect prediction or quality assurance1. We were thus pleased when the MSR program committee accepted our – back then rather exotic – paper, for which we mined usage patterns and topics of blog posts collected from four large open source communities via a blog aggregator.

Our goal was to understand the blogging behavior around software development projects and to explore the relationship between blogging and other development activities. We envisioned a twofold potential of social media in software engineering:

  1. As a new “modern” way of lightweight documentation and knowledge sharing.

  2. As a rich data source to understand and stimulate collaboration between developers and users.

When we were informed that our publication won the most influential paper award ten years later, we were even more surprised. We took this opportunity to thank the community and reflect on what we did in our paper and what has happened since then.

We also recorded this video:

Requirements Engineering for AI
Software Engineering for AI

Requirements Engineering for AI

Recently reported issues concerning the acceptance of Artificial Intelligence (AI) solutions after deployment, e.g. in the medical, automotive, or scientific domains, stress the importance of RE for designing and delivering Responsible AI systems. In this paper, we argue that RE should not only be carefully conducted but also tailored for Responsible AI. We outline related challenges for research and practice.