Talk by James Herbsleb
Technical work is almost always done by teams working within organizations. One of the biggest challenges is coordinating the work of dozens, hundreds, or thousands of people. Their individual efforts must fit together into a coherent product, yet its is impossible to plan the work in detail since the idea of the design and the final product are continually evolving. I present a socio-technical theory of coordination, based on decision networks, and how we used network analysis and statistical modeling to test several hypotheses derived from the theory. I explore how this theoretical view can drive coordination research and provide a theoretical basis for practical techniques to assist architects, developers, and managers. I will conclude with observations on how the theory can be extended to describe the effects transparency in environments such as GitHub on coordinating work in thousands or millions of people in ultra-large scale ecosystems.
James Herbsleb is a Professor in the Institute for Software Research in the School of Computer Science at Carnegie Mellon University, where he serves as Director of the PhD program in Societal Computing. His research interests lie primarily in the intersection of software engineering, computer-supported cooperative work, and socio-technical systems, focusing on such areas as geographically distributed development teams and large-scale open source development. He holds a PhD in psychology, and an MS in computer science. For about two decades, he has worked with many extraordinary colleagues to try to understand the complex and dynamic relationship between human collaboration and the software that the humans design, build, and use.
His research has won several awards, including the ACM Outstanding Research Award (2016), Alan Newell Award for Research Excellence (2014), Most Influential Paper award (ICSE 2010), Honorable Mention for Most Influential Paper award (ICSE 2011), ACM Distinguished Paper Award (ICSE 2011), Best Paper Award (Academy of Management, 2010), ACM Distinguished Paper Award (ESEM 2008), and Best Paper Award (CSCW 2006).