Context-Aware Functional Test Generation via Business Logic Extraction and Adaptation

2026-02-27

AI Summary

Functional testing has long relied on manual effort, primarily due to the difficulty of extracting business logic from unstructured requirements and the semantic gap between such logic and diverse GUI environments. To address this challenge, this work proposes LogiDroid, the first approach that integrates business logic extraction with GUI context awareness to enable end-to-end automatic generation of functional test cases complete with validation assertions. LogiDroid first retrieves and synthesizes business logic knowledge from requirements and then leverages real-time GUI semantics to generate executable test scripts. Evaluated on the FrUITeR and Lin datasets, the method achieves functional requirement coverage of 40% and 65%, respectively—improving upon existing techniques by over 48% and 55%—thereby substantially enhancing both test coverage and accuracy.

Technology Category

• Machine Learning: Statistical Relational/Logic Learning
• Knowledge Representation and Reasoning: Logic Programming
• Natural Language Processing: Information Extraction

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Functional testing is essential for verifying that the business logic of mobile applications aligns with user requirements, serving as the primary methodology for quality assurance in software development. Despite its importance, functional testing remains heavily dependent on manual effort due to two core challenges. First, acquiring and reusing complex business logic from unstructured requirements remains difficult, which hinders the understanding of specific functionalities. Second, a significant semantic gap exists when adapting business logic to the diverse GUI environments, which hinders the generation of test cases for specific mobile applications. To address the preceding challenges, we propose LogiDroid, a two-stage approach that generates individual functional test cases by extracting business logic and adapting it to target applications. First, in the Knowledge Retrieval and Fusion stage, we construct a dataset to retrieve relevant cases and extract business logic for the target functionality. Second, in the Context-Aware Test Generation stage, LogiDroid jointly analyzes the extracted business logic and the real-time GUI environment to generate functional test cases. This design allows LogiDroid to accurately understand application semantics and use domain expertise to generate complete test cases with verification assertions. We assess the effectiveness of LogiDroid using two widely-used datasets that cover 28 real-world applications and 190 functional requirements. Experimental results show that LogiDroid successfully tested 40% of functional requirements on the FrUITeR dataset (an improvement of over 48% compared to the state-of-the-art approaches) and 65% on the Lin dataset (an improvement of over 55% compared to the state-of-the-art approaches). These results demonstrate the significant effectiveness of LogiDroid in functional test generation.

Problem

• functional testing
• business logic extraction
• GUI adaptation
• test case generation
• semantic gap

Innovation

• Context-Aware Test Generation
• Business Logic Extraction
• Functional Testing
• GUI Adaptation
• Mobile Application Testing