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

arXiv: https://arxiv.org/abs/2004.06174 · HTML: https://mlsanity.com/paper/2004.06174/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [Artificial Intelligence Impact On The Labour Force -- Searching For The Analytical Skills Of The Future Software Engineers](https://mlsanity.com/paper/2302.13229/) (2023)

## Abstract (arXiv)

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.