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Understanding what a software engineer (a developer, an incident responder, aproduction engineer, etc.) is working on is a challenging problem -- especiallywhen considering the more complex software engineering workflows insoftware-intensive organizations: i) engineers rely on a multitude (perhapshundreds) of loosely integrated tools; ii) engineers engage in concurrent andrelatively long running workflows; ii) infrastructure (such as logging) is notfully aware of work items; iv) engineering processes (e.g., for incidentresponse) are not explicitly modeled. In this paper, we explain thecorresponding 'work-item prediction challenge' on the grounds of representativescenarios, report on related efforts at Facebook, discuss some lessons learned,and review related work to call to arms to leverage, advance, and combinetechniques from program comprehension, mining software repositories, processmining, and machine learning.

Understanding What Software Engineers Are Working on -- The Work-Item Prediction Challenge