Crystalline: Lowering the Cost for Developers to Collect and Organize Information for Decision Making

TL;DR

Crystalline is a system that automates the collection and organization of web-based information for developers, significantly reducing effort and cost while maintaining quality in decision-making tasks.

Contribution

It introduces an automated approach using NLP and behavioral signals to streamline information gathering and structuring for developers.

Findings

Developers create comparison tables 20% faster.

Operational costs reduced by 60%.

No loss in table quality.

Abstract

Developers perform online sensemaking on a daily basis, such as researching and choosing libraries and APIs. Prior research has introduced tools that help developers capture information from various sources and organize it into structures useful for subsequent decision-making. However, it remains a laborious process for developers to manually identify and clip content, maintaining its provenance and synthesizing it with other content. In this work, we introduce a new system called Crystalline that attempts to automatically collect and organize information into tabular structures as the user searches and browses the web. It leverages natural language processing to automatically group similar criteria together to reduce clutter as well as passive behavioral signals such as mouse movement and dwell time to infer what information to collect and how to visualize and prioritize it. Our user study suggests that developers are able to create comparison tables about 20% faster with a 60% reduction in operational cost without sacrificing the quality of the tables.