Duration: 53:14

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The rapid pace of software development innovation challenges empirical software research to keep up, if it is to deliver actionable and useful results to practitioners. The empirical software engineering research field has not always been able to deliver this. Recently, it has become increasingly apparent that rigorous data collection and analysis can be so expensive and time-consuming that empirical software engineering studies, which seek to understand the costs and bene?ts of software development solutions in practice, greatly lag the pace of innovation in the ?eld. In too many cases, a trusted body of empirical results can only be built up after the innovative solutions that they are studying are already well on their way to obsolescence or standard practice. However, we argue that recent advances put a sustainable and increased research pace within our reach. A suitably scaled-up and nimble empirical research approach must be based upon: ΓÇó The ΓÇ£crowdsourcingΓÇ¥ of tough empirical problems. Ben Shneiderman advocates Science 2.0: a vast space of web-based data which everyone can analyze, and where anyone might ?nd important new insights. The growth of the World Wide Web ... continues to reorder whole disciplines and industries. ... It is time for researchers in science to take network collaboration to the next phase and reap the potential intellectual and societal payoffs. [1] In Science 2.0, the pace of discovery and communication is increased by orders of magnitude over current practice [2]. A Science 2.0 approach to empirical software engineering addresses fundamental weaknesses in contemporary software engineering research. ΓÇó Automated or computer-assisted approaches to data synthesis, analysis, and interpretation. ΓÇó The ability to connect technical issues, data, and results back to the business drivers that affect an organizationΓÇÖs resource availability. ΓÇó Low-cost, non-intrusive ways for: o Getting results to practitioners; o Allowing practitioners to comment upon and refine the results; o Suggesting what practitioners should do with this information. This talk discusses each of these four areas and the technologies that make each possible, using real results from practice to illustrate the points. We furthermore suggest how these approaches can be used to better share and leverage results across the community of empirical researchers, which is necessary to enable scaling up to the tougher questions already appearing on the horizon.