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

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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Content Storage Management and Precaching Scheme in Content-Centric-Network-Based Internet of Vehicles

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IEEE Internet of Things Journal 2025年第9期12卷 12927-12947页

作者： Nam, Youngju Choi, Hyeonseok Lee, Euisin Kunsan National University School of Software Gunsan54150 Korea Republic of Research Institute for Computer and Information Communication Chungbuk National University Cheongju28644 Korea Republic of Chungbuk National University School of Information and Communication Engineering Cheongju28644 Korea Republic of

The emergence of smart cars has led to a significant increase in mobile data traffic on the backhaul links as a vast amount of contents is generated and consumed in the Internet of Vehicles (IoV). To cope with this situation, the precaching research of content-centric networks (CCNs) has been applied as a promising solution for reducing traffic consumption. However, most precaching schemes in CCN-IoV only focus on delay-sensitive content (DSC) and do not have proper content storage management (CSM), limiting their ability to minimize delays. To address these issues, we thus propose a novel CSM and precaching (CSMP) scheme consisting of three methods. To prevent the erases of precached or popular content, we have designed a CSM method based on our caching priority algorithm. Additionally, we have designed a DSC precaching method to improve mobility prediction and reduce delays using the recalculation time based on the Gaussian distribution with skewness. Finally, we have designed a delay-tolerant content (DTC) precaching method to minimize backhaul link traffic consumption by using an integer linear programming approach. The proposed CSMP scheme improved the evaluation value considering the success ratio and the backhaul link traffic by 23.21% compared with the existing schemes in our NS3-based simulations. © 2014 IEEE.

关键词： Delay tolerant networks

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Integrated communication and Control for Intelligent Formation Management of AAV Swarms: A Deep Reinforcement Learning Approach

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IEEE Wireless communications Letters 2025年第5期14卷 1351-1355页

作者： Wei, Jiangting Zhao, Yizhe Yang, Kun Quzhou324000 China Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China

Thanks to the comprehensive ability of information collection, integration and processing, autonomous aerial vehicles (AAV) swarm is playing a crucial role for remote monitoring or task executing in various fields. In this letter, the formation management of AAV swarms is studied with the aid of integrated communication and control (ICAC), where the leading AAV is responsible for controlling the following AAVs wirelessly in order to achieve the desired flight states. The real-time control signals' generation, subcarrier, allocation, and data rate optimization are obtained with the aid of the deep deterministic policy gradient (DDPG) algorithm, by avoiding the potential collisions among AAVs. Simulation results demonstrate that our scheme is able to not only ensures collision avoidance but also reduce the control mean maximum square error (MMSE) with a better convergence compared to other benchmarks. © 2012 IEEE.

关键词： Deep reinforcement learning

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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 collaborative 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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Energy Consumption Minimization Algorithm in IoT Network Integrating Data and Energy Transfer

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IEEE Access 2025年 13卷 75924-75934页

作者： Wang, Yang Yu, Qin Hu, Jie Yang, Kun University of Electronic Science and Technology of China Huzhou313001 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Intelligent Software and Engineering Nanjing University Suzhou Campus Suzhou215163 China

Nowadays, Internet of Things (IoT) devices are widely deployed and how to power such massive IoT devices has become a significant challenge. Especially for the battery-powered IoT devices, limited energy storage and continuous energy consumption result in frequent battery replacement and potential environmental pollution. Because radio frequency (RF) signals carry not only information but also energy, Wireless Power Transfer (WPT) in RF domain has been proved to be a reliable and controllable technology to deal with this problem. A typical Data and Energy Integrated Network (DEIN) commonly conceives one DEIN base station and numerable DEIN devices. The DEIN base station is capable of transmitting data and energy integrated signals to the DEIN devices with Simultaneous Wireless Information and Power Transfer (SWIPT) technology, and the DEIN devices are capable of harvesting energy from the downlink RF signals for uplink data transmission. In order to coordinate the wireless data and energy transmission and avoid conflicts, a transmission protocol based on the Time-Division Multiple Access (TDMA) scheme is designed. Furthermore, an optimization problem of minimizing the energy consumption of the DEIN base station is formulated. In order to address this problem, this paper proposes an energy-efficient algorithm by jointly optimizing of the transmission power of the DEIN base station, the uplink and downlink time slots and power splitting factors of the IoT devices. Both the effectiveness and efficiency of the proposed joint optimization algorithm have been validated with the simulation. © 2013 IEEE.

关键词： Energy utilization

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An Efficient Dialogue Policy Agent with Model-Based Causal Reinforcement Learning 31

An Efficient Dialogue Policy Agent with Model-Based Causal R...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Xu, Kai Wang, Zhenyu Zhao, Yangyang Fang, Bopeng School of Software Engineering South China University of Technology Guangdong China Department of Computer and Communication Engineering Changsha University of Science and Technology Changsha China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798891761964

Dialogue policy trains an agent to select dialogue actions frequently implemented via deep reinforcement learning (DRL). The model-based reinforcement methods built a world model to generate simulated data to alleviate the sample inefficiency. However, traditional world model methods merely consider one-step dialogues, leading to an inaccurate environmental simulation. Furthermore, different users may have different intention preferences, while most existing studies lack consideration of the intention-preferences causal relationship. This paper proposes a novel framework for dialogue policy learning named MCA, implemented through model-based reinforcement learning with automatically constructed causal chains. The MCA model utilizes an autoregressive Transformer to model dialogue trajectories, enabling a more accurate simulation of the environment. Additionally, it constructs a causal chains module that outputs latent preference distributions for intention-action pairs, thereby elucidating the relationship between user intentions and agent actions. The experimental results show that MCA can achieve state-of-the-art performances on three dialogue datasets over the compared dialogue agents, highlighting its effectiveness and robustness. © 2025 Association for Computational Linguistics.

关键词： Deep reinforcement learning

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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in engineering & Sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of Computing Sastra Deemed to be UniversityThanjavur613401India Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India Department of AI and Software Engineering Kangwon National UniversitySamcheok25913Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the school of Biomedical engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Two-Tier Task Offloading for Satellite-Assisted Marine Networks: A Hybrid Stackelberg–Bargaining Game Approach

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IEEE Internet of Things Journal 2025年第9期12卷 13047-13060页

作者： Wang, Zhen Lin, Bin Ye, Qiang Peng, Haixia Dalian Maritime University Department of Information Science and Technology Dalian116026 China Dalian Neusoft University of Information Department of Communication Engineering Dalian116024 China University of Calgary Schulich School of Engineering Department of Electrical and Software Engineering CalgaryABT2N 1N4 Canada Xi’an Jiaotong University School of Information and Communications Engineering Xi’an710049 China

The proliferation of maritime activities has spurred the emergence of numerous computation-intensive and delay-sensitive marine applications and services. Given the inherent rationality, selfish nature, and limited computational abilities of marine devices, devising effective strategies to incentivize their participation in task processing become a critical challenge. In this article, we investigate the satellite-assisted marine multiaccess edge computing (MEC) and propose a two-tier task offloading scheme through a hybrid Stackelberg-Bargaining game approach to enhance offloading efficiency and maximize the utility of marine devices. Specifically, for the underwater acoustic communication, we consider the scenario where multiple autonomous underwater vehicles (AUVs), managed by maritime autonomous surface ships (MASSs), upload their collected data using nonorthogonal multiple access (NOMA) to optimize channel utilization. For the data transmission above the sea surface, we consider the scenario where a low-Earth orbit satellite (LEOS) functions as a space edge server to provide computing services, and MASS offloads workloads to LEOS through frequency division multiple access (FDMA) to prevent co-channel interference. we define the utility of AUVs, MASSs and LEOSs, and model the offloading process between AUVs and MASSs as a Stackelberg game, while representing the offloading interaction between MASSs and LEOSs as a Bargaining game. Additionally, we propose efficient algorithms to optimize AUV offloading strategies and MASS pricing strategies, while refining the bidding strategies for both MASSs and LEOSs. Simulation results demonstrate that the proposed algorithms significantly outperform benchmark schemes in achieving optimal solutions. © 2014 IEEE.

关键词： Underwater acoustic communication

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ArmVR: Innovative Design Combining Virtual Reality Technology and Mechanical Equipment in Stroke Rehabilitation Therapy

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IEEE Transactions on Visualization and Computer Graphics 2025年第5期31卷 2288-2298页

作者： Qu, Jing Bu, Lingguo Chen, Zhongxin Jin, Yalu Zhao, Lei Zhu, Shantong Guo, Fenghe Joint SDU-NTU Centre for Artificial Intelligence Research Shandong University Jinan250101 China School of Software Shandong University Jinan250101 China School of Mechanical Engineering North University of China Shanxi Taiyuan China Shandong Yiyang Health Group Zhangqiu Yile Hospital Shandong Jinan China School of Mechanical and Electronic Engineering Shandong Jianzhu University Shandong Jinan China School of Animation and Digital Arts Communication University of China Beijing China

The rising incidence of stroke has created a significant global public health challenge. The immersive qualities of virtual reality (VR) technology, along with its distinct advantages, make it a promising tool for stroke rehabilitation. To address this challenge, developing VR-based upper limb rehabilitation systems has become a critical research focus. This study developed and evaluated an innovative ArmVR system that combines VR technology with rehabilitation hardware to improve recovery outcomes for stroke patients. Through comprehensive assessments, including neurofeedback, pressure feedback, and subjective feedback, the results suggest that VR technology has the potential to positively support the recovery of cognitive and motor functions. Different VR environments affect rehabilitation outcomes: forest scenarios aid emotional relaxation, while city scenarios better activate motor centers in stroke patients. The study also identified variations in responses among different user groups. Normal users showed significant changes in cognitive function, whereas stroke patients primarily experienced motor function recovery. These findings suggest that VR-integrated rehabilitation systems possess great potential, and personalized design can further enhance recovery outcomes, meet diverse patient needs, and ultimately improve quality of life. © 1995-2012 IEEE.

关键词： Functional electric stimulation

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