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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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Synthesizing Invariants for Polynomial Programs by Semidefinite Programming

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ACM Transactions on Programming Languages and Systems 2025年第1期47卷 1-35页

作者： Wu, Hao Wang, Qiuye Xue, Bai Zhan, Naijun Zhi, Lihong Yang, Zhi-Hong State Key Lab. of Computer Sciences Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Fermat Labs Huawei Inc. Dongguan China Key Lab. of System Software and State Key Lab. of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Key Laboratory of High Confidence Software Technology School of Computer Science Peking University Beijing China School of Mathematics and Statistics Central South University Changsha China

Constraint-solving-based program invariant synthesis takes a parametric invariant template and encodes the (inductive) invariant conditions into constraints. The problem of characterizing the set of all valid parameter assignments is referred to as the strong invariant synthesis problem, while the problem of finding a concrete valid parameter assignment is called the weak invariant synthesis problem. For both problems, the challenge lies in solving or reducing the encoded constraints, which are generally non-convex and lack efficient solvers. In this article, we propose two novel algorithms for synthesizing invariants of polynomial programs using semidefinite programming (SDP): (1) The Cluster algorithm targets the strong invariant synthesis problem for polynomial invariant templates. Leveraging robust optimization techniques, it solves a series of SDP relaxations and yields a sequence of increasingly precise under-approximations of the set of valid parameter assignments. We prove the algorithm’s soundness, convergence, and weak completeness under a specific robustness assumption on templates. Moreover, the outputs can simplify the weak invariant synthesis problem. (2) The Mask algorithm addresses the weak invariant synthesis problem in scenarios where the aforementioned robustness assumption does not hold, rendering the Cluster algorithm ineffective. It identifies a specific subclass of invariant templates, termed masked templates, involving parameterized polynomial equalities and known inequalities. By applying variable substitution, the algorithm transforms constraints into an equivalent form amenable to SDP relaxations. Both algorithms have been implemented and demonstrated superior performance compared to state-of-the-art methods in our empirical evaluation. © 2025 Copyright held by the owner/author(s).

关键词： Polynomial approximation

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Improving prompt tuning-based software vulnerability assessment by fusing source code and vulnerability description

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Automated software engineering 2025年第2期32卷 1-29页

作者： Wang, Jiyu Chen, Xiang Pei, Wenlong Yang, Shaoyu School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong China State Key Lab. for Novel Software Technology Nanjing University Jiangsu Nanjing China

To effectively allocate resources for vulnerability remediation, it is crucial to prioritize vulnerability fixes based on vulnerability severity. With the increasingnumber of vulnerabilities in recent years, there is an urgent need for automated methods for software vulnerability assessment (SVA). Most of the previous SVA studies mainly rely on traditional machine learning methods. Recently, fine-tuning pre-trained language models has emerged as an intuitive method for improving performance. However, there is a gap between pre-training and fine-tuning, and their performance heavily depends on the dataset’s quality of the downstream task. Therefore, we propose a prompt tuning-based method PT-SVA. Different from the fine-tuning paradigm, the prompt-tuning paradigm involves adding prompts to make the training process similar to pre-training, thereby better adapting to downstream tasks. Moreover, previous research aimed to automatically predict severity by only analyzing either the vulnerability descriptions or the source code of the vulnerability. Therefore, we further consider both types of vulnerability information for designing hybrid prompts (i.e., a combination of hard and soft prompts). To evaluate PT-SVA, we construct the SVA dataset based on the CVSS V3 standard, while previous SVA studies only consider the CVSS V2 standard. Experimental results show that PT-SVA outperforms ten state-of-the-art SVA baselines, such as by 13.7% to 42.1% in terms of MCC. Finally, our ablation experiments confirm the effectiveness of PT-SVA’s design, specifically in replacing fine-tuning with prompt tuning, incorporating both types of vulnerability information, and adopting hybrid prompts. Our promising results indicate that prompt tuning-based SVA is a promising direction and needs more follow-up studies. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Bi-modal information Hybrid prompt Pre-trained language model Prompt tuning software vulnerability assessment

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A sharding blockchain-based UAV system for search and rescue missions

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Frontiers of Computer Science 2025年第3期19卷 103-118页

作者： Xihan ZHANG Jiashuo ZHANG Jianbo GAO Libin XIA Zhi GUAN Hao HU Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China

Sharding is a promising technique to tackle the critical weakness of scalab.lity in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)*** breaking up the blockchain network into smaller partitions called shards that run independently and in parallel,shardingbased UAV systems can support a large number of search and rescue UAVs with improved scalab.lity,thereby enhancing the rescue ***,the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR *** refers to making adjustments to the blockchain towards real-time surrounding situations,while interoperability refers to making cross-shard interactions at the mission *** address the above challenges,we propose a blockchain UAV system for SAR missions based on dynamic sharding *** from the benefits in scalab.lity brought by sharding,our system improves adaptability by dynamically creating configurable and mission-exclusive shards,and improves interoperability by supporting calls between smart contracts that are deployed on different *** implement a prototype of our system based on Quorum,give an analysis of the improved adaptability and interoperability,and conduct experiments to evaluate the *** results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.

关键词： blockchain sharding unmanned aerial vehicle search and rescue blockchain interoperability

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A Helmet Detection Algorithm Based on Transformers with Deformable Attention Module

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Chinese Journal of Electronics 2025年第1期34卷 229-241页

作者： Songle Chen Hongbo Sun Yuxin Wu Lei Shang Xiukai Ruan Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Nanjing University of Posts and Telecommunications State Key Lab for Novel Software Technology Nanjing University Institute of Intelligent Locks Wenzhou University

Wearing a helmet is one of the effective measures to protect workers' safety. To address the challenges of severe occlusion, multi-scale, and small target issues in helmet detection, this paper proposes a helmet detection algorithm based on deformable attention transformers. The main contributions of this paper are as follows. A compact end-to-end network architecture for safety helmet detection based on transformers is proposed. It cancels the computationally intensive transformer encoder module in the existing detection transformer(DETR) and uses the transformer decoder module directly on the output of feature extraction for query decoding, which effectively improves the efficiency of helmet detection. A novel feature extraction network named Swin transformer with deformable attention module(DSwin transformer) is proposed. By sparse cross-window attention, it enhances the contextual awareness of multi-scale features extracted by Swin transformer, and keeps high computational efficiency simultaneously. The proposed method generates the query reference points and query embeddings based on the joint prediction probabilities, and selects an appropriate number of decoding feature maps and sparse sampling points for query decoding, which further enhance the inference capability and processing speed. On the benchmark safety-helmet-wearing-dataset(SHWD), the proposed method achieves the average detection accuracy mAP@0.5 of 95.4% with 133.35G floating-point operations per second(FLOPs) and 20 frames per second(FPS), the state-of-the-art method for safety helmet detection.

关键词： Head Accuracy Network architecture Benchmark testing Transformers Feature extraction Safety Decoding Computational efficiency Detection algorithms

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A 316 stainless steel rod impacts with a rigid flat: Theory,experiment, and numerical simulation

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Science China(Physics,Mechanics & Astronomy) 2025年第5期Mechanics & Astronomy) .卷 168-186页

作者： Yifan Wang Xuan Ye Hao Yan Tao Wang Guangyan Huang Zhuo Zhuang State Key Laboratory of Explosion Science and Safety Protection Beijing Institute of Technology Institute of Nuclear and New Energy Technology Tsinghua University Applied Mechanics Lab. School of Aerospace Engineering Tsinghua University

A slender rod suffers global vibration in impact. In this study, we present the experimental, numerical, and theoretical studies of the axial responses of a 316 stainless steel rod during vertical impact with a rigid flat. Combining the contact models and the one-dimensional(1D) wave equation, we first develop a semi-analytical vertical impact model for the rods based on a unified theoretical framework, which considers different geometries of the impacting end including the hemispherical nose, the truncated conical nose, and the flat end. Furthermore, we perform free-drop experiments on these rods and numerical simulations to verify the theoretical models. The results show that the strain-rate effect hardens the rod nose and should not be ignored even at a velocity as low as a few meters per second. After the proposal of a dynamic correction factor to adjust the quasi-static contact model, the theoretical, numerical, and experimental results agree well with one another. Also, the threedimensional(3D) FEM simulations show that the slight deviations between the experimental and the predicted results are due to the slight obliqueness of the rods in the drop. Additionally, we leverage the theoretical tool and FEM simulations to compare the mechanical responses of rods with different impacting ends, and suggestions about the selection of rod noses are obtained.

关键词： vertical impact slender rod variable separation contact-impact force drop tests

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A Novel Model Predictive Control for Quasi Z-Source Inverter to Eliminate Negative Influence of Inductor Current Ripple on Output Current

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IEEE Transactions on Power Electronics 2025年第7期40卷 9672-9684页

作者： Qiao, Shejuan Huang, Jin Xu, Yuhao Xidian University State Key Lab. of Electromechanical Intgd. Manufacturing of High-performance Electronic Equipments Xi'an710071 China Xi'an Aeronautical Institute School of Electronics Engineering Xi'an710077 China

The dead-beat control inherits the high control precision and the fast dynamic response of model predictive control (MPC). In many literatures, the dc-side current of the quasi Z-source inverter (qZSI) using the dead-beat control is regarded as an irrelevant variable to the ac-side current. In this article, a completely opposite conclusion about the relationship between the dc-side current and the ac-side current of the qZSI is given through the analysis of the output power and the inductor current error of the qZSI. The mechanism of how the inductor current of the qZSI affects the control accuracy of the output current is explored. The conclusion indicates that the control error in the average value of the inductor current leads to a deviation in the amplitude of the output current. To improve the control accuracy of the inductor current and the output current, a novel MPC is proposed for the qZSI. Based on the area method and the error symmetry principle, the average value of the inductor current in one control cycle can be equal to its reference value. Therefore, a complete match between the input power and the output power can ensure that the output current follows its reference very well. The experimental results confirm the effectiveness of the proposed method in improving the steady-state performance. © 1986-2012 IEEE.

关键词： Predictive control systems

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Decentralized Federated Learning for Wireless Traffic Prediction

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IEEE Communications Letters 2025年第5期29卷 1057-1061页

作者： Zhang, Haochang Huang, Sirui Zhou, Xiaotian Zhang, Chuanting Jia, Junrong Shandong University School of Control Science and Engineering China Shandong University Shandong Key Lab. of Intelligent Communications and Sensing-Computation Integration China Shandong University Institute of Intelligent Communication Technologies China Shandong University School of Software China

Wireless traffic prediction is indispensable for future intelligent cellular networks, as it can guide the resource allocation smartly to boost the usage efficiency. While the deep learning based methods have been reported to have promising performance, they encounter issues such as data privacy and data heterogeneity. To overcome these, in this letter we design a decentralized federated learning based network (DFLNet) for wireless traffic prediction, where a two layered federated learning framework is proposed. In the proposed algorithm, the base stations are divided into clusters, where the intra-cluster parameter aggregation is achieved through attention mechanism and that of inter-cluster is realized by reinforcement learning. The proposed approach enables the collab.rative model updates to be carried out among the most spatial correlated clients, without involving the adversarial information provided by the geometrical remote clients. Simulations confirm the improved accuracy of the proposed algorithm compared to the benchmark schemes. © 1997-2012 IEEE.

关键词： Federated learning

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SACM: spatial attributes of complex movements in multi-object tracking

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Multimedia Tools and Applications 2025年 1-20页

作者： Li, Hongjun Li, Jiaxin Sun, Xiaohu School of Information Science and Technology Nantong University Jiangsu Nantong226019 China State Key Lab. for Novel Software Technology Nanjing University Jiangsu Nantong210023 China Nantong Research Institute for Advanced Communication Technologies Jiangsu Nantong226019 China

Multi-object tracking is a crucial task in computer vision, but it faces significant challenges due to complex scenes and interactions between tracked objects. To address these challenges, we propose a complex movement-based spatial attribute tracking model (SACM). First, to enhance target detection, the model extracts both global and local features from the input feature maps to capture detailed spatial location information between different targets. Second, to strengthen the positional association of the same target across consecutive frames, position information from the previous frame is incorporated into the current frame, improving occlusion handling. Finally, a new target matching method is developed within the tracker, leveraging spatial relationships among targets to achieve precise matching of overlapping objects. Experimental results show that, compared to FairMOT, SACM achieves a 3.7% improvement in HOTA and a 4.8% improvement in IDF1 on the DanceTrack dataset. On the MOT17 dataset, SACM also improves HOTA by 3.7% and IDF1 by 4.8%. Furthermore, SACM achieves a processing speed of 30.5 FPS on a single GPU, resulting in improved tracking performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Target tracking

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ESA: Example Sieve Approach for Multi-Positive and Unlab.led Learning 25

ESA: Example Sieve Approach for Multi-Positive and Unlabeled...

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18th ACM International Conference on Web Search and Data Mining, WSDM 2025

作者： Li, Zhongnian Wei, Meng Ying, Peng Xu, Xinzheng School of Computer Science and Technology China University of Mining and Technology Xuzhou China Mine Digitization Engineering Research Center of the Ministry of Education Xuzhou China State Key Lab. for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9798400713293

Learning from Multi-Positive and Unlab.led (MPU) data has gradually attracted significant attention from practical applications. Unfortunately, the risk of MPU also suffer from the shift of minimum risk, particularly when the models are very flexible as shown in Fig.1. In this paper, to alleviate the shifting of minimum risk problem, we propose an Example Sieve Approach (ESA) to select examples for training a multi-class classifier. Specifically, we sieve out some examples by utilizing the Certain Loss (CL) value of each example in the training stage and analyze the consistency of the proposed risk estimator. Besides, we show that the estimation error of proposed ESA obtains the optimal parametric convergence rate. Extensive experiments on various real-world datasets show the proposed approach outperforms previous methods. Source code is availab.e at https://***/WilsonMqz/ESA. © 2025 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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