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Understanding and Identifying Technical Debt in the Co-Evolution of Production and Test Code

IEEE Transactions on software Engineering 2025年

作者： Guo, Yimeng Chen, Zhifei Xiao, Lu Chen, Lin Li, Yanhui Zhou, Yuming Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Stevens Institute of Technology School of Systems and Enterprises HobokenNJ07030 United States

The co-evolution of production and test code (PT co-evolution) has received increasing attention in recent years. However, we found that existing work did not comprehensively study various PT co-evolution scenarios, such as the qualification and persistence of their effects on software. Inspired by technical debt (TD), we refer to TD generated during the co-evolution between production and test code as PT co-evolution technical debt (PTCoTD). To better understand PT co-evolution, we first conducted an exploratory study on its characteristics on 15 open-source projects, finding unbalanced PT co-evolution prevalent and summarizing five potential PT flaws. Then we proposed an approach to identify and quantify PTCoTDs of these flaw patterns, considering evolutionary and structural relationships. We also built prediction models to describe cost trajectories and rank all PTCoTDs to prioritize expensive ones. The evaluation on the 15 projects shows that our approach can identify PTCoTDs that deserve attention. The identified PTCoTDs account for about half of the project's total maintenance costs, and the cost proportion of the expensive Top-5 is 1.8x more than the file proportion they contain. Almost all covered maintenance costs persist as PTCoTD in the future, with an average increase of 6.8% between the last two releases. Our approach also accurately predicts the costs of PTCoTD with an average prediction deviation of only 8.3%. Our study provides valuable insights into PT co-evolution scenarios and their effects, which can guide practices and inspire future work on software testing and maintenance. © 2025 IEEE.

关键词： software testing

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Unveiling Context-Related Anomalies: Knowledge Graph Empowered Decoupling of Scene and Action for Human-Related Video Anomaly Detection

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Chen, Chenglizhao Liu, Xinyu Song, Mengke Li, Luming Yuan, Shaojiang Yu, Xu Pang, Shanchen Qingdao Institute of Software College of Computer Science and Technology China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software China State Key Laboratory of Chemical Safety China

Video anomaly detection methods are mainly classified into two categories based on their primary feature types: appearance-based and action-based. Appearance-based methods rely on low-level visual features like color, texture, and shape, learning patterns specific to training scenes. While effective in familiar settings, they struggle with unknown or altered scenes due to poor generalization and limited understanding of action-scene relationships. In contrast, action-based methods focus on detecting action anomalies but often overlook contextual scene associations, leading to misjudgments (e.g., running on a street being deemed normal without considering scene context). To overcome these limitations, we propose a novel decoupling-based anomaly detection architecture (DecoAD). Its core lies in the decoupling and interweaving of scenes and actions, enabling explicit modeling of their complex relationships. By reconstructing these interactions using knowledge graphs, DecoAD achieves a deeper understanding of behaviors and contexts. This design ensures strong performance in both known and unknown scenarios, significantly enhancing generalization. To evaluate its effectiveness in dynamic scenes and its ability to handle scene-related anomalies, we introduce UFSR, the first video anomaly detection dataset featuring dynamic scenes and scene-related anomalies. DecoAD supports fully-supervised, weakly-supervised, and unsupervised settings, improving AUC on UBnormal by 1.1%, 3.1%, and 2.1% in fully-supervised, weakly-supervised, and unsupervised settings, and on UFSR by 1.2% and 8.2% in weakly-supervised and unsupervised settings. The source code and datasets are available at: https://***/liuxy3366/DecoAD. © 1991-2012 IEEE.

关键词： Video analysis

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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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Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Li, Shumeng Qi, Lei Yu, Qian Huo, Jing Shi, Yinghuan Gao, Yang Nanjing University State Key Laboratory for Novel Software Technology China Nanjing University National Institute of Healthcare Data Science China School of Computer Science and Engineering China Shandong Women's University School of Data and Computer Science China

Segment Anything Model (SAM) fine-tuning has shown remarkable performance in medical image segmentation in a fully supervised manner, but requires precise annotations. To reduce the annotation cost and maintain satisfactory performance, in this work, we leverage the capabilities of SAM for establishing semi-supervised medical image segmentation models. Rethinking the requirements of effectiveness, efficiency, and compatibility, we propose a three-stage framework, i.e., Stitching, Fine-tuning, and Re-training (SFR). The current fine-tuning approaches mostly involve 2D slice-wise fine-tuning that disregards the contextual information between adjacent slices. Our stitching strategy mitigates the mismatch between natural and 3D medical images. The stitched images are then used for fine-tuning SAM, providing robust initialization of pseudo-labels. Afterwards, we train a 3D semi-supervised segmentation model while maintaining the same parameter size as the conventional segmenter such as V-Net. Our SFR framework is plug-and-play, and easily compatible with various popular semi-supervised methods. We also develop an extended framework SFR+ with selective fine-tuning and re-training through confidence estimation. Extensive experiments validate that our SFR and SFR+ achieve significant improvements in both moderate annotation and scarce annotation across five datasets. In particular, SFR framework improves the Dice score of Mean Teacher from 29.68% to 74.40% with only one labeled data of LA dataset. The code is available at https://***/ShumengLI/SFR. © 1982-2012 IEEE.

关键词： Image segmentation

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A secure quantum homomorphic encryption ciphertext retrieval scheme

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Soft Computing 2025年第3期29卷 1497-1509页

作者： Cheng, Zhen-Wen Chen, Xiu-Bo Xu, Gang Chang, Yan Miao, Li-Hua Yang, Yi-Xian Wang, Ya-Lan School of Data and Computer Science Shandong Women’s University Jinan250300 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Cybersecurity Chengdu University of Information Technology Chengdu610225 China Huawei Technologies Co. Ltd Shenzhen518129 China Department of Computer Science Faculty of Engineering and Physical Sciences University of Surrey Surrey GuildfordGU2 7XH United Kingdom

In a recent paper (Gong et al. Quantum Inf Process 19:3, 2020), a novel ciphertext retrieval scheme based on the Grover algorithm and quantum homomorphic encryption was presented. In this scheme, when the server performs the operation of marking the solution on the user’s encrypted state in the Grover iteration, it needs to remove many gate-errors generated in the homomorphic evaluation of the T gate. And the server could judge this specific solution from the quantum circuit of marking the solution. It makes this scheme unable to achieve the low-cost and secure ciphertext retrieval. Therefore, we improve the Gong et al.’s scheme and propose a secure quantum homomorphic encryption ciphertext retrieval scheme. In our scheme, the trusted third party is introduced to cooperate with the server to execute the Grover algorithm. In each Grover iteration, the trusted third party can quickly mark the solution on the plaintext state, encrypt the marked state, and transmit it to the server. Then the server performs the remaining operations of this Grover iteration on the encrypted state. The trusted third party finally decrypts the iterated state. This cooperative approach ensures that the number of auxiliary qubits required and extra quantum gates executed in our scheme are lower than the Gong et al.’s scheme. By analyzing the security of our scheme, we confirm that the server and the trusted third party will not be informed of this solution. Thus, our scheme realizes the secure ciphertext retrieval with low computational overhead. We utilize IBM’s Qiskit framework to simulate our scheme, and the experimental result shows that our scheme is correct. It is worth noting that the low-cost and secure ciphertext retrieval will play a crucial role in modern information security and privacy protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Ciphertext

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Hardware Computation Graph for DNN Accelerator Design Automation Without Inter-PU Templates

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Li, Jun Wang, Wei Li, Wu-Jun Nanjing University National Key Laboratory for Novel Software Technology School of Computer Science Jiangsu Nanjing210023 China

Existing deep neural network (DNN) accelerator design automation (ADA) methods adopt architecture templates to predetermine parts of design choices and then explore the remaining design choices beyond templates. Based on the architecture hierarchy at the processing unit (PU) level, these templates can be classified into intra-PU templates and inter-PU templates. Since templates limit the flexibility of ADA, designing effective ADA methods without templates has become an important research topic. Although there have appeared some works to enhance the flexibility of ADA by removing intra-PU templates, to the best of our knowledge no existing works have studied ADA methods without inter-PU templates. ADA with predetermined inter-PU templates is typically inefficient in terms of resource utilization, especially for DNNs with complex topology. In this paper, we propose a novel method, called hardware computation graph (HCG), for ADA without inter-PU templates. In HCG, a novel inter-PU architecture exploration strategy is proposed to optimize on-chip memory utilization. This strategy mainly depends on an appearing-frequency guided pruning method and an appearing-frequency first generation method. Experiments show that HCG can achieve competitive latency while using only 13% ⁓ 90% of on-chip memory, compared with existing state-of-the-art ADA methods. © 1982-2012 IEEE.

关键词： Integrated circuit design

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A Fuzzy C-Means Clustering Algorithm for Real Medical Image Segmentation

A Fuzzy C-Means Clustering Algorithm for Real Medical Image ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Feifei Zhang Fei Shi Dayong Ren Yue Li School of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the field of medical image processing, the presence of noise can often result in the obfuscation of crucial details, which in turn can have a detrimental impact on the accuracy of clinical diagnoses. In order to effectively remove noise and improve segmentation performance, this paper proposes a refined Fuzzy C-Means (FCM) algorithm, designated as MKL-FCM. The method commences with Poisson denoising, which enables more effective handling of the noise characteristics inherent to medical images. Subsequently, multi-scale Kullback-Leibler divergence is utilised to analyse local image information across varying scales, facilitating the differentiation between tissues and pathological regions. Furthermore, tight wavelet frames are capable of capturing fine image details, while reverse optimisation of the objective function serves to correct errors in feature reconstruction, thereby enhancing the accuracy of the segmentation process. The experimental results demonstrate that MKL-FCM enhances both image clarity and segmentation accuracy, and outperforms existing methods in terms of efficiency.

关键词： Image segmentation Accuracy Noise reduction Noise Signal processing algorithms Clustering algorithms Linear programming Partitioning algorithms Speech processing Medical diagnostic imaging

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TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts

arXiv

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arXiv 2025年

作者： Huang, Yu-Hao Xu, Chang Wu, Yueying Li, Wu-Jun Bian, Jiang National Key Laboratory for Novel Software Technology School of Computer Science Nanjing University China Microsoft Research Asia China Peking University China

Time series generation models are crucial for applications like data augmentation and privacy preservation. Most existing time series generation models are typically designed to generate data from one specified domain. While leveraging data from other domain for better generalization is proved to work in other application areas, this approach remains challenging for time series modeling due to the large divergence in patterns among different real world time series categories. In this paper, we propose a multi-domain time series diffusion model with domain prompts, named TimeDP. In TimeDP, we utilize a time series semantic prototype module which defines time series prototypes to represent time series basis, each prototype vector serving as "word" representing some elementary time series feature. A prototype assignment module is applied to extract the extract domain specific prototype weights, for learning domain prompts as generation condition. During sampling, we extract "domain prompt" with few-shot samples from the target domain and use the domain prompts as condition to generate time series samples. Experiments demonstrate that our method outperforms baselines to provide the state-of-the-art in-domain generation quality and strong unseen domain generation capability. © 2025, CC BY.

关键词： Time series

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DasAtom: A Divide-and-Shuttle Atom Approach to Quantum Circuit Transformation

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Huang, Yunqi Gao, Dingchao Ying, Shenggang Li, Sanjiang Faculty of Engineering and Information Technology SydneyNSW2007 Australia Institute of Software Chinese Academy of Sciences Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science China

Neutral atom (NA) quantum systems are emerging as a leading platform for quantum computation, offering superior or competitive qubit count and gate fidelity compared to superconducting circuits and ion traps. However, the unique features of NA devices, such as long-range interactions, long qubit coherence time, and the ability to physically move qubits, present distinct challenges for quantum circuit compilation. In this paper, we introduce DasAtom, a novel divide-and-shuttle atom approach designed to optimise quantum circuit transformation for NA devices by leveraging these capabilities. DasAtom partitions circuits into subcircuits, each associated with a qubit mapping that allows all gates within the subcircuit to be directly executed. The algorithm then shuttles atoms to transition seamlessly from one mapping to the next, enhancing both execution efficiency and overall fidelity. For a 30-qubit Quantum Fourier Transform (QFT), DasAtom achieves a 415.8x improvement in fidelity over the move-based algorithm Enola and a 10.6x improvement over the SWAP-based algorithm Tetris. Notably, this improvement is expected to increase exponentially with the number of qubits, positioning DasAtom as a highly promising solution for scaling quantum computation on NA platforms. © 1982-2012 IEEE.

关键词： Qubits

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Camera Seismocardiogram Based Monitoring of Left Ventricular Ejection Time

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IEEE Transactions on Biomedical Engineering 2025年 PP卷 PP页

作者： Zhou, Zhiqin Huang, Jia Li, Haozhe Liu, Lin Zhu, Yingen Shan, Caifeng Wang, Wenjin Nanjing University State Key Laboratory for Novel Software Technology School of Intelligence Science and Technology China Southern University of Science and Technology Department of Biomedical Engineering China The Third People's Hospital of Shenzhen Intensive Care Unit China Shandong University of Science and Technology College of Electrical Engineering and Automation China

Left Ventricular Ejection Time (LVET), reflecting the duration from the onset to the end of blood ejection by the left ventricle during each heartbeat, is a critical parameter for measuring cardiac pumping efficiency. Continuous and regular monitoring of LVET is particularly crucial in assessing cardiac health, valvular function, and myocardial contractility. Seismocardiogram (SCG) signals can be utilized for LVET monitoring, as the temporal distance between the aortic valve opening (AO) and aortic valve closure (AC) in SCG signals can accurately depict LVET. This study proposes a novel way to extract LVET from laser speckle videos recorded by a remote camera based on the principle of defocused speckle imaging, thereby enabling non-contact monitoring of LVET. We extract both the low-frequency components of laser speckle motion (LSM-LF), regarded as SCG signals, and the high-frequency components of laser speckle motion (LSM-HF) from recorded videos. We utilize LSM-HF to assist the detection of AO and AC markers in LSM-LF. We validated the effectiveness of our AO and AC detection algorithm on a self-made dataset comprising 21 participants with 9616 SCG cycles. The benchmark shows that the detection accuracy for AO and AC reached 98.16% and 97.94%, respectively, with an mean absolute error of 0.5571 ms for LVET estimation. The results demonstrate that camera-SCG has strong potential for cardiac health monitoring. © 1964-2012 IEEE.

关键词： Benchmarking

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