Gathering empirical knowledge is a time consuming and error prone task while the results from empirical studies often are soon outdated by technological solutions. As a result, the impact of empirical results on software engineering practice is often not guaranteed. The empirical community is aware of this problem and during the last years, there has been a lot of discussion on how to improve the impact of empirical results on industrial practices. The discussion often focused on the use of data mining techniques and analysis of software engineering data, and the concept has often been labeled as “Empirical Software Engineering 2.0″.
Starting from the current status the discussion in this specific topic, we propose a way to use massive data analysis as a problem-driven data analysis technique and, more important, as a mean to improve the knowledge sharing process between research and industry. Our assertion is that automatic data mining and analysis, in conjunction to the emerging concepts of lean economy, wisdom of crowds, and open communities, can enable fast feedback cycles between researchers and practitioners (and among researchers as well) and consequently improve the transfer of empirical results into industrial practice.
We identify the key concepts on gathering fast feedback in empirical software engineering by following an experience-based line of reasoning by argument. Based on the identified key concepts, we design and present an approach to fast feedback cycles in empirical software engineering. We identify resulting challenges and infer a research roadmap in the form of a list of open research and engineering challenges.
Our results are not validated yet as they need a broader discussion in the community. To this end, our results serve as a basis to foster the discussion and collaboration within the research community.