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

TL;DR

This paper discusses the challenge of predicting what software engineers are working on by analyzing complex workflows, tools, and processes, highlighting efforts at Facebook and reviewing related techniques.

Contribution

It introduces the 'work-item prediction challenge' in software engineering, combining insights from multiple disciplines to improve understanding of engineer activities.

Findings

Insights from Facebook's efforts on work-item prediction

Lessons learned from real-world applications

Review of related techniques and future directions

Abstract

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.