arXiv subjects

Chong Wang

Publications and source records attributed to Chong Wang.

At least 109 records · Page 6Linked to original sources

arXiv2025

A Survey on the Techniques and Tools for Automated Requirements Elicitation and Analysis of Mobile Apps

[Background:] Research on automated requirements elicitation and analysis of mobile apps employed lots of techniques and tools proposed by RE researchers and practitioners. However, little is known about the characteristics of these techniques and tools as well as the RE tasks in requirements elicitation and analysis that got supported with the help of respective techniques and tools. [Aims:] The goal of this paper is to investigate the state-of-the-art of the techniques and tools used in automated requirements elicitation and analysis of mobile apps. [Method:] We carried out a systematic mapping study by following the guidelines of Kitchenham et al. [Results:] Based on 73 selected papers, we found the most frequently used techniques - semi-automatic techniques, and the main characteristics of the tools - open-sourced and non-self-developed tools for requirements analysis and text pre-processing. Plus, the most three investigated RE tasks are requirements analysis, mining and classification. [Conclusions:] Our most important conclusions are: (1) there is a growth in the use of techniques and tools in automated requirements elicitation and analysis of mobile apps, (2) semi-automatic techniques are mainly used in the publications on this research topic, (3) requirements analysis, mining and classification are the top three RE tasks with the support of automatic techniques and tools, and (4) the most popular tools are open-sourced and non-self-developed, and they are mainly used in requirements analysis and text processing.

cs.SE↗

arXiv2025

Towards the Datasets Used in Requirements Engineering of Mobile Apps: Preliminary Findings from a Systematic Mapping Study

[Background] Research on requirements engineering (RE) for mobile apps employs datasets formed by app users, developers or vendors. However, little is known about the sources of these datasets in terms of platforms and the RE activities that were researched with the help of the respective datasets. [Aims] The goal of this paper is to investigate the state-of-the-art of the datasets of mobile apps used in existing RE research. [Method] We carried out a systematic mapping study by following the guidelines of Kitchenham et al. [Results] Based on 43 selected papers, we found that Google Play and Apple App Store provide the datasets for more than 90% of published research in RE for mobile apps. We also found that the most investigated RE activities - based on datasets, are requirements elicitation and requirements analysis. [Conclusions] Our most important conclusions are: (1) there is a growth in the use of datasets for RE research of mobile apps since 2012, (2) the RE knowledge for mobile apps might be skewed due to the overuse of Google Play and Apple App Store, (3) there are attempts to supplement reviews of apps from repositories with other data sources, (4) there is a need to expand the alternative sources and experiments with complimentary use of multiple sources, if the community wants more generalizable results. Plus, it is expected to expand the research on other RE activities, beyond elicitation and analysis.

cs.SE↗

arXiv2025

DQEN: Dual Query Enhancement Network for DETR-based HOI Detection

Human-Object Interaction (HOI) detection focuses on localizing human-object pairs and recognizing their interactions. Recently, the DETR-based framework has been widely adopted in HOI detection. In DETR-based HOI models, queries with clear meaning are crucial for accurately detecting HOIs. However, prior works have typically relied on randomly initialized queries, leading to vague representations that limit the model's effectiveness. Meanwhile, humans in the HOI categories are fixed, while objects and their interactions are variable. Therefore, we propose a Dual Query Enhancement Network (DQEN) to enhance object and interaction queries. Specifically, object queries are enhanced with object-aware encoder features, enabling the model to focus more effectively on humans interacting with objects in an object-aware way. On the other hand, we design a novel Interaction Semantic Fusion module to exploit the HOI candidates that are promoted by the CLIP model. Semantic features are extracted to enhance the initialization of interaction queries, thereby improving the model's ability to understand interactions. Furthermore, we introduce an Auxiliary Prediction Unit aimed at improving the representation of interaction features. Our proposed method achieves competitive performance on both the HICO-Det and the V-COCO datasets. The source code is available at https://github.com/lzzhhh1019/DQEN.

cs.CV↗

arXiv2025

Twist-angle transferable continuum model and second flat Chern band in twisted MoTe2 and WSe2

We develop a twist-angle transferable continuum model for twisted transition metal dichalcogenide (tTMD) homobilayers, using tMoTe2 and tWSe2 as examples. All model parameters are extracted from density functional theory (DFT) calculations at a single twist angle (3.89°) and monolayer data. Our model captures both lattice relaxation effects and the long-range behavior of piezoelectric and ferroelectric potentials. Leveraging lattice relaxations obtained via machine learning force fields (MLFFs), the model can be efficiently transferred to other twist angles without requiring additional DFT calculations. It accurately reproduces the DFT band dispersions and quantum geometries across a wide range of twist angles. Furthermore, our model reveals that a second flat Chern band arises near 2° when the interlayer potential difference becomes comparable to the interlayer tunneling. This continuum model provides a clear understanding and starting point for engineering novel electronic phases in moiré TMDs through twist angles and lattice relaxations.

cond-mat.mtrl-sci↗

arXiv2025

Chern-Simons-matter conformal field theory on fuzzy sphere: Confinement transition of Kalmeyer-Laughlin chiral spin liquid

Gauge theories compose a large class of interacting conformal field theories in 3d, among which an outstanding category is critical Chern-Simons-matter theories. In this paper, we focus on one of the simplest instances: one complex critical scalar coupled to $\mathrm{U}(1)_2$ Chern-Simons gauge field. It is theoretically interesting as it is conjectured to exhibit dualities between four simple Lagrangian descriptions, but also practically important as it describes the transition between Kalmeyer-Laughlin chiral spin liquid (or $ν=1/2$ bosonic Laughlin state) and trivially gapped phase. Using the fuzzy sphere regularisation, we realise this theory as a transition on the spherical lowest Landau level between a $ν_f=2$ fermionic integer quantum Hall state and a $ν_b=1/2$ bosonic fractional quantum Hall state. We show that this transition is continuous and has emergent conformal symmetry. By studying the operator spectrum, we show that there exists only one relevant singlet with scaling dimension $Δ_S=1.52(18)$. We also discuss other higher operators and the consequences of our results.

cond-mat.str-el↗

arXiv2025

GV-VAD : Exploring Video Generation for Weakly-Supervised Video Anomaly Detection

Video anomaly detection (VAD) plays a critical role in public safety applications such as intelligent surveillance. However, the rarity, unpredictability, and high annotation cost of real-world anomalies make it difficult to scale VAD datasets, which limits the performance and generalization ability of existing models. To address this challenge, we propose a generative video-enhanced weakly-supervised video anomaly detection (GV-VAD) framework that leverages text-conditioned video generation models to produce semantically controllable and physically plausible synthetic videos. These virtual videos are used to augment training data at low cost. In addition, a synthetic sample loss scaling strategy is utilized to control the influence of generated synthetic samples for efficient training. The experiments show that the proposed framework outperforms state-of-the-art methods on UCF-Crime datasets. The code is available at https://github.com/Sumutan/GV-VAD.git.

cs.CV↗

arXiv2025

Dual Cross-image Semantic Consistency with Self-aware Pseudo Labeling for Semi-supervised Medical Image Segmentation

Semi-supervised learning has proven highly effective in tackling the challenge of limited labeled training data in medical image segmentation. In general, current approaches, which rely on intra-image pixel-wise consistency training via pseudo-labeling, overlook the consistency at more comprehensive semantic levels (e.g., object region) and suffer from severe discrepancy of extracted features resulting from an imbalanced number of labeled and unlabeled data. To overcome these limitations, we present a new \underline{Du}al \underline{C}ross-\underline{i}mage \underline{S}emantic \underline{C}onsistency (DuCiSC) learning framework, for semi-supervised medical image segmentation. Concretely, beyond enforcing pixel-wise semantic consistency, DuCiSC proposes dual paradigms to encourage region-level semantic consistency across: 1) labeled and unlabeled images; and 2) labeled and fused images, by explicitly aligning their prototypes. Relying on the dual paradigms, DuCiSC can effectively establish consistent cross-image semantics via prototype representations, thereby addressing the feature discrepancy issue. Moreover, we devise a novel self-aware confidence estimation strategy to accurately select reliable pseudo labels, allowing for exploiting the training dynamics of unlabeled data. Our DuCiSC method is extensively validated on four datasets, including two popular binary benchmarks in segmenting the left atrium and pancreas, a multi-class Automatic Cardiac Diagnosis Challenge dataset, and a challenging scenario of segmenting the inferior alveolar nerve that features complicated anatomical structures, showing superior segmentation results over previous state-of-the-art approaches. Our code is publicly available at \href{https://github.com/ShanghaiTech-IMPACT/DuCiSC}{https://github.com/ShanghaiTech-IMPACT/DuCiSC}.

cs.CV↗

arXiv2025

Mixed-state phases from local reversibility

We propose a refined definition of mixed-state phase equivalence based on locally reversible channel circuits. We show that such circuits preserve topological degeneracy and the locality of all operators including both strong and weak symmetries. Under a locally reversible channel, weak unitary symmetries are locally dressed into channel symmetries, a new generalization of symmetry for open quantum systems. For abelian higher-form symmetries, we show the refined definition preserves anomalies and spontaneous breaking of such symmetries within a phase. As a primary example, a two-dimensional classical loop ensemble is trivial under the previously adopted definition of mixed-state phases. However, it has non-trivial topological degeneracy arising from a mutual anomaly between strong and weak 1-form symmetries, and our results show that it is not connected to a trivial state via locally reversible channel circuits.

quant-ph↗

arXiv2025

Contextual Code Retrieval for Commit Message Generation: A Preliminary Study

A commit message describes the main code changes in a commit and plays a crucial role in software maintenance. Existing commit message generation (CMG) approaches typically frame it as a direct mapping which inputs a code diff and produces a brief descriptive sentence as output. However, we argue that relying solely on the code diff is insufficient, as raw code diff fails to capture the full context needed for generating high-quality and informative commit messages. In this paper, we propose a contextual code retrieval-based method called C3Gen to enhance CMG by retrieving commit-relevant code snippets from the repository and incorporating them into the model input to provide richer contextual information at the repository scope. In the experiments, we evaluated the effectiveness of C3Gen across various models using four objective and three subjective metrics. Meanwhile, we design and conduct a human evaluation to investigate how C3Gen-generated commit messages are perceived by human developers. The results show that by incorporating contextual code into the input, C3Gen enables models to effectively leverage additional information to generate more comprehensive and informative commit messages with greater practical value in real-world development scenarios. Further analysis underscores concerns about the reliability of similaritybased metrics and provides empirical insights for CMG.

cs.SE↗

arXiv2025

A Vision for Auto Research with LLM Agents

This paper introduces Agent-Based Auto Research, a structured multi-agent framework designed to automate, coordinate, and optimize the full lifecycle of scientific research. Leveraging the capabilities of large language models (LLMs) and modular agent collaboration, the system spans all major research phases, including literature review, ideation, methodology planning, experimentation, paper writing, peer review response, and dissemination. By addressing issues such as fragmented workflows, uneven methodological expertise, and cognitive overload, the framework offers a systematic and scalable approach to scientific inquiry. Preliminary explorations demonstrate the feasibility and potential of Auto Research as a promising paradigm for self-improving, AI-driven research processes.

cs.AI↗

arXiv2025

Generalized probabilistic approximation characteristic based on Birkhoff orthogonality and related conclusions in $S_{\infty}$-norm

In this article, we generalize the definition of the probabilistic Gel'fand width from the Hilbert space to the strictly convex reflexive space by giving Birkhoff left orthogonal decomposition theorem. Meanwhile, a more natural definition of Gel'fand width in the classical setting is selected to make sure probabilistic and average Gel'fand widths will not lose their meaning, so that we can give the equality relation between the probabilistic Gel'fand width and the probabilistic linear width of the Hilbert space. Meanwhile, we use this relationship to continue the study of the Gel'fand widths of the univariate Sobolev space and the multivariate Sobolev space, especially in $S_{\infty}$-norm, and determine the exact order of probabilistic and average Gel'fand widths.

math.FA↗

arXiv2025

Cross Knowledge Distillation between Artificial and Spiking Neural Networks

Recently, Spiking Neural Networks (SNNs) have demonstrated rich potential in computer vision domain due to their high biological plausibility, event-driven characteristic and energy-saving efficiency. Still, limited annotated event-based datasets and immature SNN architectures result in their performance inferior to that of Artificial Neural Networks (ANNs). To enhance the performance of SNNs on their optimal data format, DVS data, we explore using RGB data and well-performing ANNs to implement knowledge distillation. In this case, solving cross-modality and cross-architecture challenges is necessary. In this paper, we propose cross knowledge distillation (CKD), which not only leverages semantic similarity and sliding replacement to mitigate the cross-modality challenge, but also uses an indirect phased knowledge distillation to mitigate the cross-architecture challenge. We validated our method on main-stream neuromorphic datasets, including N-Caltech101 and CEP-DVS. The experimental results show that our method outperforms current State-of-the-Art methods. The code will be available at https://github.com/ShawnYE618/CKD

cs.CV↗

arXiv2025

A Hierarchical and Evolvable Benchmark for Fine-Grained Code Instruction Following with Multi-Turn Feedback

Large language models (LLMs) have advanced significantly in code generation, yet their ability to follow complex programming instructions with layered and diverse constraints remains underexplored. Existing benchmarks often prioritize functional correctness, overlooking the nuanced requirements found in real-world development. We introduce MultiCodeIF, a comprehensive benchmark designed to evaluate instruction-following in code generation across multiple dimensions: constraint type, hierarchical levels, and iterative refinement. Built upon a structured taxonomy of 9 categories and 27 constraint types, MultiCodeIF enables granular assessment of both functional and non-functional instruction adherence. Using an automated pipeline, ConstraGen, we synthesize and evolve 2,021 code tasks sourced from 14 programming languages, supporting multi-turn evaluation through feedback-driven task variants. Empirical evaluation of six state-of-the-art LLMs uncovers substantial performance disparities. The top-performing model, Claude-3-7-Sonnet, achieves 63.0% average constraint satisfaction, while smaller models like Qwen3-1.7B fall to 44.8%. Models perform well on explicit constraints, but struggle with implicit or abstract constraints. Tasks with multiple hierarchical constraints significantly reduce model success rates, from 54.5% in single-level to just 18.8% in multi-level scenarios. However, structured feedback enables progressive improvement: average constraint satisfaction rises from 63.0% to 83.4% over four iterative refinement rounds. MultiCodeIF provides a scalable, constraint-aware, and feedback-sensitive framework to benchmark LLMs under realistic code generation scenarios, bridging the gap between synthetic evaluations and real-world instruction complexity. The full benchmark dataset, evaluation pipeline, and source code are available at https://github.com/SYSUSELab/MultiCodeIF.

cs.SE↗

arXiv2025

Detection of 2D SPT phases under decoherence

We propose a bulk order parameter for extracting symmetry-protected topological (SPT) invariants of quantum many-body mixed states on a two dimensional lattice using partial symmetries. The procedure builds on the partial symmetry order parameter recently developed by some of the authors to study SPT phases of pure states and adapts them to the decohered setting. For a symmetry $G = E \times A$ where $E$ is a strong symmetry and $A$ is a weak symmetry, we show that the partial symmetry order parameter detects SPT invariants jointly protected by $E$ and $A$. We demonstrate this explicitly using a class of mixed states obtained from CZX-type models with $\mathbb{Z}_2\times\mathbb{Z}_2$ symmetry and subjecting them to noise that weakens one of the $\mathbb{Z}_2$ symmetries. We also comment on the practical detection of SPT invariants in quantum simulators through randomized measurements.

cond-mat.str-el↗

arXiv2025

VFArchē: A Dual-Mode Framework for Locating Vulnerable Functions in Open-Source Software

Software Composition Analysis (SCA) has become pivotal in addressing vulnerabilities inherent in software project dependencies. In particular, reachability analysis is increasingly used in Open-Source Software (OSS) projects to identify reachable vulnerabilities (e.g., CVEs) through call graphs, enabling a focus on exploitable risks. Performing reachability analysis typically requires the vulnerable function (VF) to track the call chains from downstream applications. However, such crucial information is usually unavailable in modern vulnerability databases like NVD. While directly extracting VF from modified functions in vulnerability patches is intuitive, patches are not always available. Moreover, our preliminary study shows that over 26% of VF do not exist in the modified functions. Meanwhile, simply ignoring patches to search vulnerable functions suffers from overwhelming noises and lexical gaps between descriptions and source code. Given that almost half of the vulnerabilities are equipped with patches, a holistic solution that handles both scenarios with and without patches is required. To meet real-world needs and automatically localize VF, we present VFArchē, a dual-mode approach designed for disclosed vulnerabilities, applicable in scenarios with or without available patch links. The experimental results of VFArchē on our constructed benchmark dataset demonstrate significant efficacy regarding three metrics, achieving 1.3x and 1.9x Mean Reciprocal Rank over the best baselines for Patch-present and Patch-absent modes, respectively. Moreover, VFArchē has proven its applicability in real-world scenarios by successfully locating VF for 43 out of 50 latest vulnerabilities with reasonable efforts and significantly reducing 78-89% false positives of SCA tools.

cs.SE↗

arXiv2025

CommVQ: Commutative Vector Quantization for KV Cache Compression

Large Language Models (LLMs) are increasingly used in applications requiring long context lengths, but the key-value (KV) cache often becomes a memory bottleneck on GPUs as context grows. To address this, we propose Commutative Vector Quantization (CommVQ) to significantly reduce memory usage for long-context LLM inference. We first introduce additive quantization with a lightweight encoder and codebook to compress the KV cache, which can be decoded via simple matrix multiplication. To further reduce computational costs during decoding, we design the codebook to be commutative with Rotary Position Embedding (RoPE) and train it using an Expectation-Maximization (EM) algorithm. This enables efficient integration of decoding into the self-attention mechanism. Our approach achieves high accuracy with additive quantization and low overhead via the RoPE-commutative codebook. Experiments on long-context benchmarks and GSM8K show that our method reduces FP16 KV cache size by 87.5% with 2-bit quantization, while outperforming state-of-the-art KV cache quantization methods. Notably, it enables 1-bit KV cache quantization with minimal accuracy loss, allowing a LLaMA-3.1 8B model to run with a 128K context length on a single RTX 4090 GPU. The source code is available at: https://github.com/UMass-Embodied-AGI/CommVQ.

cs.CL↗

arXiv2025

Minimizing False Positives in Static Bug Detection via LLM-Enhanced Path Feasibility Analysis

Static bug analyzers play a crucial role in ensuring software quality. However, existing analyzers for bug detection in large codebases often suffer from high false positive rates. This is primarily due to the limited capabilities of analyzers in path feasibility validation with multiple conditional branches and complex data dependencies. While current LLM-based approaches attempt to address this issue, their effectiveness remains limited due to insufficient constraint cascade analysis and scalability challenges in large projects. To address this challenge, we propose an iterative path feasibility analysis framework LLM4PFA. By leveraging LLM agent based targeted constraint reasoning, and key context-aware analysis driven by agent planning, LLM4PFA effectively enhances complex inter-procedural path feasibility analysis for minimizing false positives in static bug detection. Evaluation results show that LLM4PFA precisely filters out 72% to 96% false positives reported during static bug detection, significantly outperforming all the baselines by 41.1% - 105.7% improvements; meanwhile LLM4PFA only misses 3 real bugs of 45 true positives.

cs.SE↗

arXiv2025

Instruction-Following Pruning for Large Language Models

With the rapid scaling of large language models (LLMs), structured pruning has become a widely used technique to learn efficient, smaller models from larger ones, delivering superior performance compared to training similarly sized models from scratch. In this paper, we move beyond the traditional static pruning approach of determining a fixed pruning mask for a model, and propose a dynamic approach to structured pruning. In our method, the pruning mask is input-dependent and adapts dynamically based on the information described in a user instruction. Our approach, termed "instruction-following pruning", introduces a sparse mask predictor that takes the user instruction as input and dynamically selects the most relevant model parameters for the given task. To identify and activate effective parameters, we jointly optimize the sparse mask predictor and the LLM, leveraging both instruction-following data and the pre-training corpus. Experimental results demonstrate the effectiveness of our approach on a wide range of evaluation benchmarks. For example, our 3B activated model improves over the 3B dense model by 5-8 points of absolute margin on domains such as math and coding, and rivals the performance of a 9B model.

cs.CL↗