arXiv subjects

Lingchao Chen

Publications and source records attributed to Lingchao Chen.

3 recordsLinked to original sources

arXiv2026

Scaling Mobile Chaos Testing with AI-Driven Test Execution

Mobile applications in large-scale distributed systems are susceptible to backend service failures, yet traditional chaos engineering approaches cannot scale mobile testing due to the combinatorial explosion of flows, locations, and failure scenarios that need validation. We present an automated mobile chaos testing system that integrates DragonCrawl, an LLM-based mobile testing platform, with uHavoc, a service-level fault injection system. The key insight is that adaptive AI-driven test execution can navigate mobile applications under degraded backend conditions, eliminating the need to manually write test cases for each combination of user flow, city, and failure type. Since Q1 2024, our system has executed over 180,000 automated chaos tests across 47 critical flows in Uber's Rider, Driver, and Eats applications, representing approximately 39,000 hours of manual testing effort that would be impractical at this scale. We identified 23 resilience risks, with 70% being architectural dependency violations where non-critical service failures degraded core user flows. Twelve issues were severe enough to prevent trip requests or food orders. Two caused application crashes detectable only through mobile chaos testing, not backend testing alone. Automated root cause analysis reduced debugging time from hours to minutes, achieving 88% precision@5 in attributing mobile failures to specific backend services. This paper presents the system design, evaluates its performance under fault injection (maintaining 99% test reliability), and reports operational experience demonstrating that continuous mobile resilience validation is achievable at production scale.

cs.SE↗

arXiv2025

MSV-Mamba: A Multiscale Vision Mamba Network for Echocardiography Segmentation

Ultrasound imaging frequently encounters challenges, such as those related to elevated noise levels, diminished spatiotemporal resolution, and the complexity of anatomical structures. These factors significantly hinder the model's ability to accurately capture and analyze structural relationships and dynamic patterns across various regions of the heart. Mamba, an emerging model, is one of the most cutting-edge approaches that is widely applied to diverse vision and language tasks. To this end, this paper introduces a U-shaped deep learning model incorporating a large-window Mamba scale (LMS) module and a hierarchical feature fusion approach for echocardiographic segmentation. First, a cascaded residual block serves as an encoder and is employed to incrementally extract multiscale detailed features. Second, a large-window multiscale mamba module is integrated into the decoder to capture global dependencies across regions and enhance the segmentation capability for complex anatomical structures. Furthermore, our model introduces auxiliary losses at each decoder layer and employs a dual attention mechanism to fuse multilayer features both spatially and across channels. This approach enhances segmentation performance and accuracy in delineating complex anatomical structures. Finally, the experimental results using the EchoNet-Dynamic and CAMUS datasets demonstrate that the model outperforms other methods in terms of both accuracy and robustness. For the segmentation of the left ventricular endocardium (${LV}_{endo}$), the model achieved optimal values of 95.01 and 93.36, respectively, while for the left ventricular epicardium (${LV}_{epi}$), values of 87.35 and 87.80, respectively, were achieved. This represents an improvement ranging between 0.54 and 1.11 compared with the best-performing model.

eess.IV↗

arXiv2020

Fast and Precise On-the-fly Patch Validation for All

Generate-and-validate (G&V) automated program repair (APR) techniques have been extensively studied during the past decade. Meanwhile, such techniques can be extremely time-consuming due to manipulation of the program code to fabricate a large number of patches and also repeated executions of tests on patches to identify potential fixes. PraPR, a recent G&V APR technique, reduces these costs by modifying program code directly at the level of compiled bytecode, and further performing on-the-fly patching by allowing multiple patches to be tested within the same JVM session. However, PraPR is limited due to its pattern-based, bytecode-level nature and it is basically unsound/imprecise as it assumes that patch executions do not change global JVM state and affect later patch executions on the same JVM session. Inspired by the PraPR work, we propose a unified patch validation framework, named UniAPR, which aims to speed up the patch validation for both bytecode and source-code APR via on-the-fly patching; furthermore, UniAPR addresses the imprecise patch validation issue by resetting the JVM global state via runtime bytecode transformation. We have implemented UniAPR as a fully automated Maven Plugin. We have also performed the first study of on-the-fly patch validation for state-of-the-art source-code-level APR. Our experiments show the first empirical evidence that vanilla on-the-fly patch validation can be imprecise/unsound; in contrast, our UniAPR framework can speed up state-of-the-art APR by over an order of magnitude without incurring any imprecision in patch validation, enabling all existing APR techniques to explore a larger search space to fix more bugs in the near future. Furthermore, UniAPR directly enables hybrid source and bytecode APR to fix substantially more bugs than all state-of-the-art APR techniques (under the same time limit) in the near future.

cs.SE↗