Understanding What Software Engineers Are Working on -- The Work-Item Prediction Challenge
Facebook Inc.（脸书公司）
Ralf Lämmel, Alvin Kerber, Liane Praza
备注：
This paper appears in Proceedings of 28th International Conference on Program Comprehension, ICPC 2020. The subject of the paper is covered by the first author's keynote at the same conference
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.
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