Uncertain&lt; <i>T</i> &gt;

TLDR

Emerging applications increasingly rely on estimates from sensor data, probabilistic models, machine learning, big data, and human data, yet representing these uncertain values with discrete types (floats, integers, booleans) leads developers to ignore their probabilistic nature and introduces uncertainty bugs. The authors identify three mechanisms of uncertainty bugs: treating estimates as facts ignores random error, propagating that error through computations, and posing Boolean questions on probabilistic data that generate false positives and negatives.

Abstract

Emerging applications increasingly use estimates such as sensor data (GPS), probabilistic models, machine learning, big data, and human data. Unfortunately, representing this uncertain data with discrete types (floats, integers, and booleans) encourages developers to pretend it is not probabilistic, which causes three types of uncertainty bugs. (1) Using estimates as facts ignores random error in estimates. (2) Computation compounds that error. (3) Boolean questions on probabilistic data induce false positives and negatives.