arXiv subjects

Hieu Huynh

Publications and source records attributed to Hieu Huynh.

4 recordsLinked to original sources

arXiv2026

XAgent: eXecution-guided Agentic AI for Effective Localization and Resolution of GitHub Issues

Agentic AI has enabled capabilities in leveraging Large Language Models (LLMs) to autonomously resolve repository-level GitHub issues. However, due to the reliance on limited static description of issues, existing agentic approaches suffer from incorrect localization and incomplete validation. Solely relying on this information can bias LLM reasoning toward the narrow scope of the issue description, leading to incomplete patches that fail to address the underlying issue. In this paper, we present XAgent, an execution-guided agentic framework that analyzes dynamic behavior and additional program context to localize and validate issues. The experimental results on the SWE-bench-lite dataset demonstrate that XAgent outperforms other existing approaches, achieving a resolve rate of 62.0% and a function localization accuracy of 72.8%, while maintaining cost efficiency. Our analysis further shows that XAgent successfully resolves 7 additional issues that the top existing baselines fail to address. This work highlights a shift from static, description-oriented patch generation toward dynamic execution-guided issue resolution, opening new opportunities for LLM-based coding agents to achieve more robust and generalizable software maintenance.

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arXiv2025

RBCTest: Leveraging LLMs to Mine and Verify Oracles of API Response Bodies for RESTful API Testing

In API testing, deriving logical constraints on API response bodies to be used as oracles is crucial for generating test cases and performing automated testing of RESTful APIs. However, existing approaches are restricted to dynamic analysis, in which oracles are extracted via the execution of APIs as part of the system under test. In this paper, we propose a complementary LLM-based static approach in which constraints for API response bodies are mined from API specifications. We leverage large language models (LLMs) to comprehend API specifications, mine constraints for response bodies, and generate test cases. To reduce LLM hallucination, we apply an Observation-Confirmation (OC) scheme that uses initial prompts to contextualize constraints, allowing subsequent prompts to more accurately confirm their presence. Our empirical results show that RBCTest with OC prompting achieves high precision in constraint mining, with averages ranging from 85.1% to 93.6%. It also performs well in generating test cases from mined constraints, with precision ranging from 86.4% to 91.7%. We further use test cases generated by RBCTest to detect 46 mismatches between API specifications and actual response data across 19 real-world APIs. Four of these mismatches were reported in developers' forums.

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arXiv2025

Towards Test Generation from Task Description for Mobile Testing with Multi-modal Reasoning

In Android GUI testing, generating an action sequence for a task that can be replayed as a test script is common. Generating sequences of actions and respective test scripts from task goals described in natural language can eliminate the need for manually writing test scripts. However, existing approaches based on large language models (LLM) often struggle with identifying the final action, and either end prematurely or continue past the final screen. In this paper, we introduce VisiDroid, a multi-modal, LLM-based, multi-agent framework that iteratively determines the next action and leverages visual images of screens to detect the task's completeness. The multi-modal approach enhances our model in two significant ways. First, this approach enables it to avoid prematurely terminating a task when textual content alone provides misleading indications of task completion. Additionally, visual input helps the tool avoid errors when changes in the GUI do not directly affect functionality toward task completion, such as adjustments to font sizes or colors. Second, the multi-modal approach also ensures the tool not progress beyond the final screen, which might lack explicit textual indicators of task completion but could display a visual element indicating task completion, which is common in GUI apps. Our evaluation shows that VisiDroid achieves an accuracy of 87.3%, outperforming the best baseline relatively by 23.5%. We also demonstrate that our multi-modal framework with images and texts enables the LLM to better determine when a task is completed.

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arXiv2024

Segment-Based Test Case Prioritization: A Multi-objective Approach

Regression testing of software is a crucial but time-consuming task, especially in the context of user interface (UI) testing where multiple microservices must be validated simultaneously. Test case prioritization (TCP) is a cost-efficient solution to address this by scheduling test cases in an execution order that maximizes an objective function, generally aimed at increasing the fault detection rate. While several techniques have been proposed for TCP, most rely on source code information which is usually not available for UI testing. In this paper, we introduce a multi-objective optimization approach to prioritize UI test cases, using evolutionary search algorithms and four coverage criteria focusing on web page elements as objectives for the optimization problem. Our method, which does not require source code information, is evaluated using two evolutionary algorithms (AGE-MOEA and NSGA-II) and compared with other TCP methods on a self-collected dataset of 11 test suites. The results show that our approach significantly outperforms other methods in terms of Average Percentage of Faults Detected (APFD) and APFD with Cost (APFDc), achieving the highest scores of 87.8\% and 79.2\%, respectively. We also introduce a new dataset and demonstrate the significant improvement of our approach over existing ones via empirical experiments. The paper's contributions include the application of web page segmentation in TCP, the construction of a new dataset for UI TCP, and empirical comparisons that demonstrate the improvement of our approach.

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