arXiv subjects

Haowen Yang

Publications and source records attributed to Haowen Yang.

8 recordsLinked to original sources

arXiv2026

EffiHolmes: Differential Profiling-Guided Repository Level Time Inefficiency Fix Localization

Large software systems often suffer from time inefficiencies that cause excessive execution time despite functional correctness. Localizing their fix locations is difficult because, unlike functional bugs, they produce neither test failures nor stack-trace clues, making traditional and recent LLM-based fault localization methods unsuitable. Runtime profiling provides alternative evidence but faces three challenges in repository-level settings: single-run profiling cannot reliably distinguish inefficiency hotspots from execution noise; existing profilers struggle to extract relevant execution paths from extensive background execution; and a semantic gap remains between observed hotspots and actual fix locations. We propose EffiHolmes, an LLM-based framework for repository-level time inefficiency fix localization. EffiHolmes uses differential profiling under default and scaled workloads to identify inefficiency hotspots, extracts compact execution paths connecting these hotspots to the reported inefficient function, and employs domain-guided LLM reasoning to locate the underlying inefficiency logic. We also introduce RepoEffi-Bench, the first benchmark for repository-level inefficiency localization, containing 140 high-quality issues collected from popular Python repositories. Experiments show that EffiHolmes consistently outperforms state-of-the-art retrieval-, agent-, and profiling-based baselines, improving file-level Acc@3 by 4.29 percentage points with GPT-5.1 and function-level Acc@5 by 15.00 percentage points with qwen3-4b. It also remains robust across model capacities.

cs.SE↗

arXiv2026

Skyrmion Quantum Diode Prototype: Bridging Micromagnetic Simulations and Quantum Models

Magnetic skyrmions are topologically protected spin textures known for their robustness against perturbations. Their topological stability makes them robust information carriers, ideal for tackling a key challenge in quantum computing: creating reliable, one-way links between different types of qubits. In this proof-of-concept study, we introduce a novel device - the skyrmion quantum diode - based on skyrmion qubits. Our approach combines classical micromagnetic simulations, achieving skyrmion diameters as small as 3 nm, with quantum circuit models inspired by superconducting qubits. In this work, we demonstrate: (i) unidirectional skyrmion transport via the skyrmion Hall effect in asymmetric junctions, spanning length scales from 20 nm down to 3 nm; (ii) potential compatibility with flux-tunable quantum architectures; and (iii) preliminary insights into anharmonicity in skyrmion-based qubit systems. These results establish both the operational feasibility and the scaling behavior necessary for a hybrid skyrmion-quantum platform. Our work outlines a path toward integrating skyrmion based quantum components into practical device architectures, enabling low-dissipation, unidirectional quantum information transport. This capability is crucial for scalable quantum computing, spintronic logic, and hybrid quantum systems, and opens opportunities for chipscale, pump-free isolators and directional quantum links that enhance readout fidelity, reduce cryogenic load, and support modular skyrmion-superconducting processors

quant-ph↗

arXiv2025

A Goal-Driven Survey on Root Cause Analysis

Root Cause Analysis (RCA) is a crucial aspect of incident management in large-scale cloud services. While the term root cause analysis or RCA has been widely used, different studies formulate the task differently. This is because the term "RCA" implicitly covers tasks with distinct underlying goals. For instance, the goal of localizing a faulty service for rapid triage is fundamentally different from identifying a specific functional bug for a definitive fix. However, previous surveys have largely overlooked these goal-based distinctions, conventionally categorizing papers by input data types (e.g., metric-based vs. trace-based methods). This leads to the grouping of works with disparate objectives, thereby obscuring the true progress and gaps in the field. Meanwhile, the typical audience of an RCA survey is either laymen who want to know the goals and big picture of the task or RCA researchers who want to figure out past research under the same task formulation. Thus, an RCA survey that organizes the related papers according to their goals is in high demand. To this end, this paper presents a goal-driven framework that effectively categorizes and integrates 135 papers on RCA in the context of cloud incident management based on their diverse goals, spanning the period from 2014 to 2025. In addition to the goal-driven categorization, it discusses the ultimate goal of all RCA papers as an umbrella covering different RCA formulations. Moreover, the paper discusses open challenges and future directions in RCA.

cs.SE↗

arXiv2025

An Empirical Study on Commit Message Generation using LLMs via In-Context Learning

Commit messages concisely describe code changes in natural language and are important for software maintenance. Several approaches have been proposed to automatically generate commit messages, but they still suffer from critical limitations, such as time-consuming training and poor generalization ability. To tackle these limitations, we propose to borrow the weapon of large language models (LLMs) and in-context learning (ICL). Our intuition is based on the fact that the training corpora of LLMs contain extensive code changes and their pairwise commit messages, which makes LLMs capture the knowledge about commits, while ICL can exploit the knowledge hidden in the LLMs and enable them to perform downstream tasks without model tuning. However, it remains unclear how well LLMs perform on commit message generation via ICL. In this paper, we conduct an empirical study to investigate the capability of LLMs to generate commit messages via ICL. Specifically, we first explore the impact of different settings on the performance of ICL-based commit message generation. We then compare ICL-based commit message generation with state-of-the-art approaches on a popular multilingual dataset and a new dataset we created to mitigate potential data leakage. The results show that ICL-based commit message generation significantly outperforms state-of-the-art approaches on subjective evaluation and achieves better generalization ability. We further analyze the root causes for LLM's underperformance and propose several implications, which shed light on future research directions for using LLMs to generate commit messages.

cs.SE↗

arXiv2024

An Empirical Study on Package-Level Deprecation in Python Ecosystem

Open-source software (OSS) plays a crucial role in modern software development. Utilizing OSS code can greatly accelerate software development, reduce redundancy, and enhance reliability. Python, a widely adopted programming language, is renowned for its extensive and diverse third-party package ecosystem. However, a significant number of OSS packages within the Python ecosystem are in poor maintenance, leading to potential risks in functionality and security. Consequently, it is essential to establish a deprecation mechanism to assist package developers and users in managing packages effectively. To facilitate the establishment of the package-level deprecation mechanism, this paper presents a mixed-method empirical study, including data analysis and surveys. We investigate the current practices of announcing, receiving, and handling package-level deprecation in the Python ecosystem. We also assess the benefits of having deprecation announcements for inactively maintained packages. Furthermore, we investigate the challenges faced by package developers and users and their expectations for future deprecation practices. Our findings reveal that 75.4% of inactive package developers have no intention of releasing deprecation declarations for various reasons, while 89.5% of users express a desire to be notified about the deprecation, highlighting a gap between developers and users; in many cases, no alternative solutions are available when deprecation occurs, emphasizing the need to explore practical approaches that enable seamless package handover and require less maintenance effort. Our work aims to enhance the understanding of existing package-level deprecation patterns within the Python OSS realm and facilitate the development of deprecation practices for the Python community in the future.

cs.SE↗

arXiv2023

LiveVV: Human-Centered Live Volumetric Video Streaming System

Volumetric video has emerged as a prominent medium within the realm of eXtended Reality (XR) with the advancements in computer graphics and depth capture hardware. Users can fully immersive themselves in volumetric video with the ability to switch their viewport in six degree-of-freedom (DOF), including three rotational dimensions (yaw, pitch, roll) and three translational dimensions (X, Y, Z). Different from traditional 2D videos that are composed of pixel matrices, volumetric videos employ point clouds, meshes, or voxels to represent a volumetric scene, resulting in significantly larger data sizes. While previous works have successfully achieved volumetric video streaming in video-on-demand scenarios, the live streaming of volumetric video remains an unresolved challenge due to the limited network bandwidth and stringent latency constraints. In this paper, we for the first time propose a holistic live volumetric video streaming system, LiveVV, which achieves multi-view capture, scene segmentation \& reuse, adaptive transmission, and rendering. LiveVV contains multiple lightweight volumetric video capture modules that are capable of being deployed without prior preparation. To reduce bandwidth consumption, LiveVV processes static and dynamic volumetric content separately by reusing static data with low disparity and decimating data with low visual saliency. Besides, to deal with network fluctuation, LiveVV integrates a volumetric video adaptive bitrate streaming algorithm (VABR) to enable fluent playback with the maximum quality of experience. Extensive real-world experiment shows that LiveVV can achieve live volumetric video streaming at a frame rate of 24 fps with a latency of less than 350ms.

cs.MM↗

arXiv2023

Understanding User Behavior in Volumetric Video Watching: Dataset, Analysis and Prediction

Volumetric video emerges as a new attractive video paradigm in recent years since it provides an immersive and interactive 3D viewing experience with six degree-of-freedom (DoF). Unlike traditional 2D or panoramic videos, volumetric videos require dense point clouds, voxels, meshes, or huge neural models to depict volumetric scenes, which results in a prohibitively high bandwidth burden for video delivery. Users' behavior analysis, especially the viewport and gaze analysis, then plays a significant role in prioritizing the content streaming within users' viewport and degrading the remaining content to maximize user QoE with limited bandwidth. Although understanding user behavior is crucial, to the best of our best knowledge, there are no available 3D volumetric video viewing datasets containing fine-grained user interactivity features, not to mention further analysis and behavior prediction. In this paper, we for the first time release a volumetric video viewing behavior dataset, with a large scale, multiple dimensions, and diverse conditions. We conduct an in-depth analysis to understand user behaviors when viewing volumetric videos. Interesting findings on user viewport, gaze, and motion preference related to different videos and users are revealed. We finally design a transformer-based viewport prediction model that fuses the features of both gaze and motion, which is able to achieve high accuracy at various conditions. Our prediction model is expected to further benefit volumetric video streaming optimization. Our dataset, along with the corresponding visualization tools is accessible at https://cuhksz-inml.github.io/user-behavior-in-vv-watching/

cs.MM↗

arXiv2023

FSVVD: A Dataset of Full Scene Volumetric Video

Recent years have witnessed a rapid development of immersive multimedia which bridges the gap between the real world and virtual space. Volumetric videos, as an emerging representative 3D video paradigm that empowers extended reality, stand out to provide unprecedented immersive and interactive video watching experience. Despite the tremendous potential, the research towards 3D volumetric video is still in its infancy, relying on sufficient and complete datasets for further exploration. However, existing related volumetric video datasets mostly only include a single object, lacking details about the scene and the interaction between them. In this paper, we focus on the current most widely used data format, point cloud, and for the first time release a full-scene volumetric video dataset that includes multiple people and their daily activities interacting with the external environments. Comprehensive dataset description and analysis are conducted, with potential usage of this dataset. The dataset and additional tools can be accessed via the following website: https://cuhksz-inml.github.io/full_scene_volumetric_video_dataset/.

cs.MM↗