LLM-based Abstraction and Concretization for GUI Test Migration

2024-09-08

arXiv.org

AI Summary

GUI test transfer across applications faces challenges such as mapping failure and incomplete test coverage due to structural heterogeneity among UI controls. This paper proposes a novel “abstraction–concretization” paradigm: instead of performing interface-level control mapping, it first abstracts universal test logic from multi-source, functionally equivalent test cases; then, leveraging a large language model (LLM), it concretizes this logic into executable test scripts for the target app via functional semantic alignment and joint GUI event–assertion generation. To our knowledge, this is the first approach to deeply integrate LLMs throughout both test logic generalization and target-specific adaptation, enabling functional-semantic-level transfer. Evaluated on the FrUITeR and Lin datasets, our method achieves functional test success rates of 64% (+191%) and 75% (+42%), respectively—demonstrating substantial improvements in transfer effectiveness and practicality.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Humans and AI: Intelligent User Interfaces
• Natural Language Processing: Generation

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

GUI test migration aims to produce test cases with events and assertions to test specific functionalities of a target app. Existing migration approaches typically focus on the widget-mapping paradigm that maps widgets from source apps to target apps. However, since different apps may implement the same functionality in different ways, direct mapping may result in incomplete or buggy test cases, thus significantly impacting the effectiveness of testing target functionality and the practical applicability. In this paper, we propose a new migration paradigm (i.e., abstraction-concretization paradigm) that first abstracts the test logic for the target functionality and then utilizes this logic to generate the concrete GUI test case. Furthermore, we introduce MACdroid, the first approach that migrates GUI test cases based on this paradigm. Specifically, we propose an abstraction technique that utilizes source test cases from source apps targeting the same functionality to extract a general test logic for that functionality. Then, we propose a concretization technique that utilizes the general test logic to guide an LLM in generating the corresponding GUI test case (including events and assertions) for the target app. We evaluate MACdroid on two widely-used datasets (including 31 apps, 34 functionalities, and 123 test cases). On the FrUITeR dataset, the test cases generated by MACdroid successfully test 64% of the target functionalities, improving the baselines by 191%. On the Lin dataset, MACdroid successfully tests 75% of the target functionalities, outperforming the baselines by 42%. These results underscore the effectiveness of MACdroid in GUI test migration.

Problem

• Overcoming widget-mapping limitations in GUI test migration
• Abstracting test logic for target functionality in apps
• Generating effective GUI test cases using LLM guidance

Innovation

• Abstraction-concretization paradigm for GUI test migration
• MACdroid migrates GUI test cases via general test logic
• LLM generates GUI test cases from abstracted logic