Understanding What Software Engineers Are Working on -- The Work-Item Prediction Challenge
This addresses a practical problem for software-intensive organizations by identifying gaps in current methods, but it is incremental as it reviews existing efforts and proposes no new solution.
The paper tackles the challenge of predicting what software engineers are working on in complex workflows, highlighting issues like tool fragmentation and lack of explicit process modeling, and calls for combining techniques from program comprehension and machine learning to address this.
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.