  Understanding what a software engineer (a developer, an incident responder, a
production engineer, etc.) is working on is a challenging problem -- especially
when considering the more complex software engineering workflows in
software-intensive organizations: i) engineers rely on a multitude (perhaps
hundreds) of loosely integrated tools; ii) engineers engage in concurrent and
relatively long running workflows; ii) infrastructure (such as logging) is not
fully aware of work items; iv) engineering processes (e.g., for incident
response) are not explicitly modeled. In this paper, we explain the
corresponding 'work-item prediction challenge' on the grounds of representative
scenarios, report on related efforts at Facebook, discuss some lessons learned,
and review related work to call to arms to leverage, advance, and combine
techniques from program comprehension, mining software repositories, process
mining, and machine learning.
