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E-PRedictor: an approach for early prediction of pull request acceptance

Science China(Information Sciences) 2025年第5期68卷 380-395页

作者： Kexing CHEN Lingfeng BAO Xing HU Xin XIA Xiaohu YANG State Key Laboratory of Blockchain and Data Security Zhejiang University Software Engineering Application Technology Lab

A pull request(PR) is an event in Git where a contributor asks project maintainers to review code he/she wants to merge into a project. The PR mechanism greatly improves the efficiency of distributed software development in the opensource community. Nevertheless, the massive number of PRs in an open-source software(OSS) project increases the workload of developers. To reduce the burden on developers, many previous studies have investigated factors that affect the chance of PRs getting accepted and built prediction models based on these factors. However, most prediction models are built on the data after PRs are submitted for a while(e.g., comments on PRs), making them not useful in practice. Because integrators still need to spend a large amount of effort on inspecting PRs. In this study, we propose an approach named E-PRedictor(earlier PR predictor) to predict whether a PR will be merged when it is created. E-PRedictor combines three dimensions of manual statistic features(i.e., contributor profile, specific pull request, and project profile) and deep semantic features generated by BERT models based on the description and code changes of PRs. To evaluate the performance of E-PRedictor, we collect475192 PRs from 49 popular open-source projects on GitHub. The experiment results show that our proposed approach can effectively predict whether a PR will be merged or not. E-PRedictor outperforms the baseline models(e.g., Random Forest and VDCNN) built on manual features significantly. In terms of F1@Merge, F1@Reject, and AUC(area under the receiver operating characteristic curve), the performance of E-PRedictor is 90.1%, 60.5%, and 85.4%, respectively.

关键词： pull request prediction model GitHub

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ALSTNet:Autoencoder fused long-and short-term time-series network for the prediction of tunnel structure

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Deep Underground Science and engineering 2025年第1期4卷 72-82页

作者： Bowen Du Haohan Liang Yuhang Wang Junchen Ye Xuyan Tan Weizhong Chen State Key Laboratory of Software Development Environment Beihang UniversityBeijingChina State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhanChina

It is crucial to predict future mechanical behaviors for the prevention of structural *** for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to the complex *** that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models,this study proposed an improved prediction model through the autoencoder fused long-and short-term time-series network driven by the mass number of monitoring ***,the proposed model was formalized on multiple time series of strain monitoring ***,the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction *** the results indicate,the proposed model shows obvious superiority in predicting the future mechanical behaviors of *** a case study,the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.

关键词： autoencoder deep learning structural health monitoring time-series prediction

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New analytical solutions for free vibration of embedded magneto-electro-elastic cylindrical shells with step-wise thickness variations

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Applied Mathematics and Mechanics(English Edition) 2025年第3期46卷 447-466页

作者： Jufang JIA Huilin YIN Qinyu YU Jiabin SUN Xinsheng XU Zhenhuan ZHOU School of Mechanical Engineering and Automation Dalian Polytechnic UniversityDalian 116034Liaoning ProvinceChina State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentSchool of Mechanics and Aerospace EngineeringDalian University of TechnologyDalian 116024Liaoning ProvinceChina State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentSchool of Chemical EngineeringOcean and Life SciencesDalian University of TechnologyPanjin 124221Liaoning ProvinceChina

In recent years,magneto-electro-elastic(MEE)cylindrical shells with step-wise thicknesses have shown significant potential in the field of vibration energy *** aid the design of such energy harvesting devices,an accurate free vibration analysis of embedded MEE cylindrical shells with step-wise thicknesses is performed within the framework of symplectic *** using the Legendre transformation,a new known vector is defined to transform the higher-order partial differential governing equations into a set of lower-order ordinary differential ***,the original vibration analysis is regarded as an eigen problem in the symplectic space,and analytical solutions can be represented by the symplectic *** numerical examples,the new analytical solutions are compared with the existing results,and good agreement is ***,the effects of critical design parameters on free vibration characteristics are thoroughly *** numerical results can serve as benchmarks for the development of other approximate or numerical methods.

关键词： analytical solution free vibration magneto-electro-elastic(MEE)material cylindrical shell with variable thickness symplectic method

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AsyCo: an asymmetric dual-task co-training model for partial-label learning

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Science China(Information Sciences) 2025年第5期68卷 332-347页

作者： Beibei LI Yiyuan ZHENG Beihong JIN Tao XIANG Haobo WANG Lei FENG College of Computer Science Chongqing University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Technology Zhejiang University School of Computer Science and Engineering Nanyang Technological University

Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.

关键词： machine learning weakly supervised learning partial-label learning co-training models candidate label sets

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Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(Information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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Understanding the effect of cathodic reaction on corrosion-enhanced erosion in sand-entraining electrolyte

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材料科学技术（英文版） 2025年第6期209卷 103-116页

作者： Qiliang Zhang Long Hao Wanbin Chen Yi Huang Yunze Xu State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024China School of Naval Architecture and Ocean Engineering Dalian University of TechnologyDalian 116024China State Key Laboratory for Corrosion and Protection Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China

In this study,the pure erosion behaviour of pure iron and its erosion-corrosion behaviour under different anodic polarization currents were investigated in various cathodic reactions(oxygen reduction,hydrogen ion reduction,and water reduction)using a cylindrical stirring *** corrosion-enhanced erosion(C-E)rates were determined for each *** results revealed that pure iron displayed similar pure erosion behaviour across all three cathodic *** the cathodic reactions involve hydrogen ion reduction or water reduction,the erosion-corrosion of pure iron manifested as uniform damage,with the reduction in hardness being the main cause of the C-E in this ***,in the case of oxy-gen reduction reaction as the cathodic reaction,the erosion-corrosion presented as pitting damage,with the reduction in hardness resulting from localized concentration of anodic current and the formation of easily worn protruding flaky iron structures at the edges of the pits as the main mechanism of the ***,linear and exponential relationships were found between the C-E rate and the anodic current density for uniform damage and pitting damage,***,the concept of surface equivalent hardness was proposed,along with the establishment of a mathematical model for surface equivalent hardness based on the relationships between the C-E rate and the anodic current *** the surface equivalent hardness enables the evaluation of the erosion rate on material surfaces considering the coupled effect.

关键词： Corrosion-enhanced erosion Cathodic reaction Pitting damage Uniform damage Hardness

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Unveiling the role of high-order anharmonicity in thermal expansion: A first-principles perspective

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Chinese Physics B 2025年第4期34卷 212-219页

作者： Tianxu Zhang Kun Zhou Yingjian Li Chenhao Yi Muhammad Faizan Yuhao Fu Xinjiang Wang Lijun Zhang State Key Laboratory of Integrated Optoelectronics Key Laboratory of Automobile Materials of the Ministry of Education and College of Materials Science and EngineeringJilin UniversityChangchun 130012China Jiangxi Guanyi Grinding Co. Ltd.Fengxin 330700China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchun 130012China International Center of Computational Method and Software Jilin UniversityChangchun 130012China

Thermal expansion is crucial for various industrial processes and is increasingly the focus of research endeavors aimed at improving material ***,it is the continuous advancements in first-principles calculations that have enabled researchers to understand the microscopic origins of thermal *** this study,we propose a coefficient of thermal expansion(CTE)calculation scheme based on self-consistent phonon theory,incorporating the fourth-order *** selected four structures(Si,CaZrF_(6),SrTiO_(3),NaBr)to investigate high-order anharmonicity’s impact on their CTEs,based on bonding *** results indicate that our method goes beyond the second-order quasi-harmonic approximation and the third-order perturbation theory,aligning closely with experimental ***,we observed that an increase in the ionicity of the structures leads to a more pronounced influence of high-order anharmonicity on CTE,with this effect primarily manifesting in variations of the Grüneisen *** research provides a theoretical foundation for accurately predicting and regulating the thermal expansion behavior of materials.

关键词： high-order anharmonicity Grüneisen parameter thermal expansion first-principles calculations

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Understanding interfacial engineering of surface functionalized boron nitride nanosheets within thermal interface materials

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Science China Materials 2025年第4期68卷 1300-1302页

作者： Gaojie Han Yuezhan Fengg Changyu Shen State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450002China

The continuous miniaturization and high-power development of electronic devices have given rise to severe interface thermal issues,which urgently demand highly thermally conductive thermal interface materials(TIMs)to ... 详细信息

关键词： interfacial engineering heat accumulation interface thermal issueswhich thermal interface materials surface functionalized boron nitride nanosheets electronic devices thermal conductivity

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A comparison on the effects of coal fines and sand fouling on the shear behaviors of railway ballast using large scale direct shear tests

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Journal of Traffic and Transportation engineering(English Edition) 2025年第1期12卷 52-67页

作者： Xu Zhang Shunying Ji Yang Feng Yongjun Li Chunfa Zhao School of Civil and Transportation Engineering Guangdong University of TechnologyGuangzhou 510006China State Key Laboratory of Rail Transit Vehicle System Southwest jiaotong UniversityChengdu 610031China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024China

Railway ballast is easily contaminated by different fouling material and the fouled ballast always causes the deterioration of railway *** previous studies focused on the effect of a certain kind of fouling material on ballast performance,but few studies compared the influence of different fouling *** paper presents a series of direct shear tests on the clean ballast and the fouled ballast mixed with coal fines or sand to understand the effects of different fouling material.A unified empirical model is proposed to well capture the shear stress-displacement responses of the clean and fouled *** equations are also obtained to characterize the variation of the nonlinear shear strength envelope of the fouled ballast with the fouling *** on these equations,a simple method is proposed to predict the shear strength of the fouled ballast using the shear test results of clean *** measured results demonstrate significant differences between the effects of coal fines and sand on the shear behaviors of ballast,to which particular attention should be paid in order to take targeted maintenance measures for the fouled *** mechanisms for the different effects of coal fines and sand are also analyzed to understand the roles that various fouling material plays in the fouled ballast.

关键词： Railway ballast Fouled ballast Shear strength Direct shear test Contamination

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Absorption-Reflection-Transmission Power Coefficient Guiding Gradient Distribution of Magnetic MXene in Layered Composites for Electromagnetic Wave Absorption

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Nano-Micro Letters 2025年第6期17卷 466-481页

作者： Yang Zhou Wen Zhang Dong Pan Zhaoyang Li Bing Zhou Ming Huang Liwei Mi Chuntai Liu Yuezhan Feng Changyu Shen State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450002People’s Republic of China Yaoshan Laboratory Pingdingshan UniversityPingdingshan 467000People’s Republic of China

The morphological distribution of absorbent in composites is equally important with absorbents for the overall electromagnetic properties,but it is often ***,a comprehensive consideration including electromagnetic component regulation,layered arrangement structure,and gradient concentration distribution was used to optimize impedance matching and enhance electromagnetic *** the microscale,the incorporation of magnetic Ni nanoparticles into MXene nanosheets(Ni@MXene)endows suitable intrinsic permittivity and *** the macroscale,the layered arrangement of Ni@MXene increases the effective interaction area with electromagnetic waves,inducing multiple reflection/scattering *** this basis,according to the analysis of absorption,reflection,and transmission(A-R-T)power coefficients of layered composites,the gradient concentration distribution was constructed to realize the impedance matching at low-concentration surface layer,electromagnetic loss at middle concentration interlayer and microwave reflection at high-concentration bottom ***,the layered gradient composite(LG5-10-15)achieves complete absorption coverage of X-band at thickness of 2.00-2.20 mm with RL_(min) of-68.67 dB at 9.85 GHz in 2.05 mm,which is 199.0%,12.6%,and 50.6%higher than non-layered,layered and layered descending gradient composites,***,this work confirms the importance of layered gradient structure in improving absorption performance and broadens the design of high-performance microwave absorption materials.

关键词： Magnetic MXene Layered and gradient structure Power coefficient Electromagnetic wave absorption

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