NaviQAte: A Functionality-Guided Approach for Automated Web Application Navigation

핵심 개념

NaviQAte is a novel approach that frames web application exploration as a question-and-answer task, generating action sequences to navigate and complete functionalities without requiring detailed task parameters.

초록

NaviQAte is a three-phase, multi-model methodology for automated web application navigation. It focuses on functionality-guided exploration, integrating multi-modal inputs such as text and images to enhance contextual understanding.

In the Action Planning phase, NaviQAte concretizes abstract functionality descriptions using retrieval-augmented generation, extracts webpage context, and predicts the next step. In the Choice Extraction phase, it preprocesses and ranks actionable elements based on semantic similarity to the predicted next step, and generates contextual descriptions for these elements. In the Decision Making phase, NaviQAte selects the optimal action by combining task history, annotated screenshots, and the ranked actionable elements.

Evaluations on the Mind2Web-Live and Mind2Web-Live-Abstracted datasets show that NaviQAte achieves a 44.23% success rate in user task navigation and a 38.46% success rate in functionality navigation, representing a 15% and 33% improvement over the next-best baseline, WebCanvas. These results demonstrate the effectiveness of NaviQAte's functionality-guided approach in advancing automated web application testing.

통계

Over 781 billion website visits globally each month.
The average reference task length in the Mind2Web-Live dataset is 7.9.

인용구

"End-to-end web testing is challenging due to the need to explore diverse web application functionalities."
"Current state-of-the-art methods, such as WebCanvas, are not designed for broad functionality exploration; they rely on specific, detailed task descriptions, limiting their adaptability in dynamic web environments."

핵심 통찰 요약

NaviQAte: Functionality-Guided Web Application Navigation

by Mobina Shahb... 게시일 arxiv.org 09-18-2024

https://arxiv.org/pdf/2409.10741.pdf

더 깊은 질문

How could the integration of a task or functionality description generation tool further enhance the capabilities of NaviQAte?

The integration of a task or functionality description generation tool could significantly enhance the capabilities of NaviQAte by automating the process of creating abstract functionality descriptions for web applications. This would streamline the initial phase of NaviQAte's workflow, allowing it to quickly adapt to new web applications without requiring extensive manual input. By leveraging advanced natural language processing techniques, such a tool could analyze the structure and features of a web application to generate comprehensive functionality descriptions that encapsulate the various user interactions available. This would not only reduce the time and effort needed to define functionalities but also improve the accuracy and relevance of the generated descriptions, leading to more effective navigation and task execution. Furthermore, having a robust set of functionality descriptions would enable NaviQAte to generalize better across different applications, enhancing its adaptability and scalability in dynamic web environments. Overall, this integration would facilitate a more seamless and efficient automated testing process, ultimately improving the quality and reliability of web applications.

What other types of multi-modal inputs, beyond text and images, could be leveraged to improve the contextual understanding and decision-making of web navigation agents like NaviQAte?

In addition to text and images, several other types of multi-modal inputs could be leveraged to enhance the contextual understanding and decision-making capabilities of web navigation agents like NaviQAte. These include:

Audio Inputs: Incorporating voice commands or audio cues could allow users to interact with the web application more naturally. This would enable the agent to understand user intent through spoken language, enhancing its ability to navigate based on verbal instructions.

Video Inputs: Utilizing video feeds of user interactions could provide insights into how users navigate web applications in real-time. This data could be analyzed to identify common patterns and behaviors, allowing the agent to learn from actual user experiences and improve its navigation strategies.

User Interaction Data: Collecting data on user interactions, such as mouse movements, click patterns, and scrolling behavior, could provide valuable context for the agent. This information could help the agent understand user preferences and optimize its navigation paths accordingly.

Sensor Data: For mobile or IoT applications, integrating sensor data (e.g., GPS location, device orientation) could enhance the agent's contextual awareness. This would allow it to tailor navigation strategies based on the user's physical environment or device capabilities.

Feedback Mechanisms: Implementing real-time feedback from users regarding the agent's actions could help refine its decision-making process. This could include thumbs-up/thumbs-down ratings or more detailed feedback on the accuracy of the actions taken.

By incorporating these diverse multi-modal inputs, web navigation agents like NaviQAte could achieve a more holistic understanding of the context in which they operate, leading to improved accuracy, efficiency, and user satisfaction in automated web navigation tasks.

How could the incorporation of an intermediate reward system, providing feedback on partial task completion, help to improve the performance and accuracy of NaviQAte?

Incorporating an intermediate reward system that provides feedback on partial task completion could significantly enhance the performance and accuracy of NaviQAte by introducing a mechanism for continuous learning and adaptation. This system would allow NaviQAte to receive real-time feedback on its actions, enabling it to assess the effectiveness of its decisions at various stages of task execution.

Motivation for Incremental Progress: By rewarding the agent for completing sub-tasks or making progress toward the overall goal, it would be motivated to optimize its actions for efficiency. This could lead to a more strategic approach to navigation, where the agent prioritizes actions that yield the highest rewards.

Error Correction: Immediate feedback on partial completions would allow NaviQAte to identify and correct errors in real-time. If the agent receives negative feedback for a specific action, it can adjust its strategy in subsequent steps, reducing the likelihood of repeating the same mistakes.

Enhanced Learning: An intermediate reward system would facilitate a reinforcement learning approach, where the agent learns from both successes and failures. This continuous learning process would enable NaviQAte to refine its decision-making algorithms over time, improving its overall effectiveness in navigating web applications.

Dynamic Adaptation: As web applications frequently change, an intermediate reward system would allow NaviQAte to adapt to new conditions and user behaviors more effectively. By evaluating its performance based on real-time feedback, the agent could adjust its navigation strategies to align with evolving user needs and application structures.

User-Centric Improvements: By incorporating user feedback into the reward system, NaviQAte could better align its actions with user expectations and preferences. This would enhance user satisfaction and trust in the automated navigation process.

Overall, an intermediate reward system would create a more responsive and intelligent web navigation agent, capable of improving its performance and accuracy through iterative learning and adaptation.