Automatic Macro Mining from Interaction Traces at Scale

TL;DR

This paper presents a novel LLM-based method for automatically extracting meaningful, executable macros from mobile interaction traces, facilitating understanding and automation of smartphone tasks at scale.

Contribution

It introduces a new approach leveraging Large Language Models to extract, describe, and validate macros from mobile interaction data, a task previously difficult at scale.

Findings

Macros are accurately extracted and semantically labeled.

Extracted macros are fully executable and useful for downstream tasks.

User evaluations confirm macro quality and usefulness.

Abstract

Macros are building block tasks of our everyday smartphone activity (e.g., "login", or "booking a flight"). Effectively extracting macros is important for understanding mobile interaction and enabling task automation. These macros are however difficult to extract at scale as they can be comprised of multiple steps yet hidden within programmatic components of mobile apps. In this paper, we introduce a novel approach based on Large Language Models (LLMs) to automatically extract semantically meaningful macros from both random and user-curated mobile interaction traces. The macros produced by our approach are automatically tagged with natural language descriptions and are fully executable. We conduct multiple studies to validate the quality of extracted macros, including user evaluation, comparative analysis against human-curated tasks, and automatic execution of these macros. These experiments and analyses show the effectiveness of our approach and the usefulness of extracted macros in various downstream applications.