Extracting Semantically Meaningful and Replayable Macros from Mobile App Interaction Traces at Scale

核心概念

A novel system that can automatically extract semantically meaningful and replayable macros reflecting useful tasks on mobile apps from random or human-curated interaction traces.

要約

The paper introduces a novel approach that uses Large Language Models (LLMs) with a trace-based chain-of-thought technique and optimal path synthesis to effectively extract large-scale, meaningful macros from interaction traces. The extracted macros are automatically tagged with natural language descriptions and are fully executable.
The key highlights of the approach are:

Task Discovery: The LLM is used to discover potential user tasks on each UI screen in the interaction traces.
Action Grounding: The LLM predicts the relevant actions for completing the discovered user tasks.
Parameter-finding: The LLM predicts the additional information required for the user tasks.
Action/Trace Merging: The system builds interaction graphs from multiple traces to find optimal paths for executing the macros.

The authors conduct multiple studies to validate the quality of the extracted macros, including user evaluation, comparative analysis against human-curated tasks, and automatic execution of the macros in live environments. The results demonstrate the effectiveness of the approach and the usefulness of the extracted macros in various downstream applications.

統計

The extracted macros from the RICO dataset contain 6.05 actions on average before optimization, and 3.41 actions after optimization, a 43.6% reduction.
The extracted macros from the Rehearsal dataset contain 7.51 actions on average before optimization, and 3.40 actions after optimization, a 54.7% reduction.

引用

"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."
"Recent advances in Large Language Models (LLMs) have enabled a new class of methods and models that can understand and interact with mobile apps with an unprecedented level of intelligence."

抽出されたキーインサイト

Automatic Macro Mining from Interaction Traces at Scale

by Forrest Huan... 場所 arxiv.org 04-17-2024

https://arxiv.org/pdf/2310.07023.pdf

深掘り質問

How can the extracted macros be leveraged to improve the user experience and discoverability of mobile app features?

The extracted macros can be leveraged in several ways to enhance the user experience and discoverability of mobile app features.

Task Automation: The macros can be used to automate repetitive tasks for users, saving time and effort. By providing a set of predefined actions to accomplish common tasks, users can streamline their interactions with the app.

How-to Knowledge Sharing: The macros can serve as a guide for users who are unfamiliar with certain features of the app. By presenting step-by-step instructions in natural language descriptions, users can easily learn how to perform specific tasks within the app.

Enhanced User Onboarding: For new users, the macros can act as a tutorial to introduce them to the key features and functionalities of the app. By following the macros, users can quickly familiarize themselves with the app's capabilities.

Improved Accessibility: The macros can benefit users with disabilities by providing a structured way to navigate and interact with the app. By automating complex tasks, the macros can make the app more accessible to a wider range of users.

Personalized Recommendations: By analyzing the extracted macros, app developers can gain insights into the most commonly performed tasks by users. This information can be used to tailor personalized recommendations and suggestions for users, enhancing their overall app experience.

How can the potential limitations and biases in the interaction traces used to extract the macros be addressed?

Diverse User Interactions: To address biases in the interaction traces, it is important to collect data from a diverse set of users with varying usage patterns. This can help in capturing a more comprehensive range of interactions and tasks performed within the app.

Balanced Dataset: Ensure that the dataset used for macro extraction is balanced across different user demographics, device types, and app usage scenarios. This can help in reducing biases and ensuring that the extracted macros are representative of the app's user base.

Quality Assurance: Implement quality assurance measures to validate the accuracy and relevance of the extracted macros. This can involve manual review by experts to ensure that the macros align with the actual user tasks and interactions.

Regular Updates: Continuously update and refine the macro extraction system to adapt to changes in app interfaces and user behaviors. By staying current with app updates and user trends, biases in the interaction traces can be minimized.

Transparency and Documentation: Provide transparency in the macro extraction process and document any limitations or biases in the dataset. This can help in understanding the potential constraints of the extracted macros and how they may impact their usability.

How can the macro extraction system be extended to support multi-app workflows and cross-device interactions?

Inter-App Communication: Extend the macro extraction system to analyze interactions across multiple apps, enabling the creation of macros that involve workflows spanning different applications. This can enhance the user experience by providing seamless transitions between tasks in different apps.

Cross-Device Integration: Incorporate cross-device interactions into the macro extraction system to support scenarios where users switch between devices while performing tasks. By capturing interactions across devices, the system can generate macros that facilitate continuity in user workflows.

Contextual Understanding: Enhance the system's ability to understand the context of interactions, allowing for the extraction of macros that consider the user's current task and environment. This can enable more intelligent and adaptive workflows that cater to the user's specific needs.

Machine Learning Models: Integrate machine learning models that can learn from user interactions across multiple apps and devices to improve the accuracy and relevance of the extracted macros. By leveraging advanced algorithms, the system can adapt to complex multi-app workflows and cross-device interactions.

User Feedback Integration: Incorporate user feedback mechanisms to gather insights on multi-app workflows and cross-device interactions. By incorporating user input, the system can continuously learn and improve its ability to extract macros that support diverse and interconnected user tasks.