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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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Minimum Spanning Tree Image Segmentation Model Based on New Weights

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Informatica (Slovenia) 2024年第13期48卷 15-30页

作者： Li, Hong School of Computer Information Engineering Shanxi Technology and Business College Taiyuan030006 China

The quality of image segmentation results has a direct impact on subsequent image processing and its application. Therefore, image segmentation represents a pivotal step in image processing and recognition. A minimum spanning tree image segmentation method using new weights is proposed to address the issues of low efficiency and low segmentation accuracy in traditional image segmentation methods. The study initially proposes a minimum spanning tree construction method based on color vector angle distance color difference measurement. This method compensates for the non-uniformity of color space through dynamic weight adjustment, and improves upon the fast multi-spanning tree segmentation algorithm with adaptive threshold. The proposed decomposition method preprocesses the image to reduce the image nodes. The experiment showed that the research method effectively improved color difference measurement accuracy. The segmentation accuracy, over segmentation rate, and under segmentation rate obtained by the research method demonstrated superior performance in terms of average values when compared to other segmentation methods. The average values were 0.984, 0.059, and 0.023, respectively. Compared to other minimum spanning tree segmentation algorithms, the research method improved the average segmentation time by 0.46 seconds, 0.49 seconds, 2.04 seconds, and 3.79 seconds, respectively. The segmentation algorithm studied in this study has good segmentation performance and improves the efficiency of image segmentation, which has certain practical application value in various image segmentation fields. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Image segmentation

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Correlation Attention Registration Based on Deep Learning from Histopathology to MRI of Prostate

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Critical Reviews in Biomedical engineering 2024年第2期52卷 39-50页

作者： Wang, Xue Song, Zhili Zhu, Jianlin Li, Zhixiang School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201418 China

Deep learning offers a promising methodology for the registration of prostate cancer images from histopathology to MRI. We explored how to effectively leverage key information from images to achieve improved end-to-end registration. We developed an approach based on a correlation attention registration framework to register segmentation labels of histopathology onto MRI. The network was trained using paired prostate datasets of histopathology and MRI from the Cancer Imaging Archive. We introduced An L2-Pearson correlation layer to enhance feature matching. Furthermore, our model employed an enhanced attention regression network to distinguish between key and nonkey features. For data analysis, we used the Kolmogorov–Smirnov test and a one-sample t-test, with the statistical significance level for the one-sample t-test set at 0.001. Compared with two other models (ProsRegNet and CNNGeo), our model exhibited improved performance in Dice coefficient, with increases of 9.893% and 2.753%, respectively. The Hausdorff distance was reduced by approximately 50% and 50%, while the average label error (ALE) was reduced by 0.389% and 15.021%. The proposed improved multimodal prostate registration framework demonstrated high performance in statistical analysis. The results indicate that our enhanced strategy significantly improves registration performance and enables faster registration of histopathological images of patients undergoing radical prostatectomy to preoperative MRI. More accurate registration can prevent over-diagnosing low-risk cancers and frequent false positives due to observer differences. © 2023 by Begell House, Inc.

关键词： Magnetic resonance imaging

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A hybrid machine learning approach for enhanced anomaly detection in drinking water quality

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International Journal of Environmental Studies 2024年第2期81卷 661-674页

作者： Kalaivanan, K. Vellingiri, J. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

Machine learning has become important for anomaly detection in water quality prediction. Data anomalies are often caused by the difficulties of analysing large amounts of data, both technical and human, but approaches have been inadequate when water contamination increases. The paper reports an anomaly detection technique based on an autoencoder and an extreme learning machine (AEELM). This approach uses an autoencoder for feature selection and an ELM algorithm for classification purposes. It is estimated using the Cauvery River dataset. The autoencoder-based ELM techniques outperformed other models with 83% accuracy, 77% precision, 83% recall, and 83% F1-level characteristic performance indicators. These results demonstrate its great effectiveness. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Water quality

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A privacy and compliance in regulated anonymous payment system based on blockchain

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Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3141-3157页

作者： Elfadul, Issameldeen Wu, Lijun Elhabob, Rashad Elkhalil, Ahmed School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer and Information Engineering Qilu Institute of Technology Shandong Jinan250200 China Faculty of Computer Science and Information Technology Karary University Khartoum Sudan

Decentralized Anonymous Payment Systems (DAP), often known as cryptocurrencies, stand out as some of the most innovative and successful applications on the blockchain. These systems have garnered significant attention in the financial industry due to their highly secure and reliable features. Regrettably, the DAP system can be exploited to fund illegal activities such as drug dealing and terrorism. Therefore, governments are increasingly worried about the illicit use of DAP systems, which poses a critical threat to their security. This paper proposes Privacy and Compliance in Regulated Anonymous Payment System Based on Blockchain (PCRAP), which provides government supervision and enforces regulations over transactions without sacrificing the essential idea of the blockchain, that is, without surrendering transaction privacy or anonymity of the participants. The key characteristic of the proposed scheme is using a ring signature and stealth address to ensure the anonymity of both the sender and receiver of the transaction. Moreover, a Merkle Tree is used to guarantee government supervision and enforce regulations. Our proposed scheme satisfies most of the stringent security requirements and complies with the standards of secure payment systems. Additionally, while our work supports government regulations and supervision, it guarantees unconditional anonymity for users. Furthermore, the performance analysis demonstrates that our suggested scheme still remains applicable and effective even when achieving complete anonymity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Blockchain

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A Generative Model-Based Network Framework for Ecological Data Reconstruction

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computers, Materials & Continua 2025年第1期82卷 929-948页

作者： Shuqiao Liu Zhao Zhang Hongyan Zhou Xuebo Chen School of Electronic and Information Engineering University of Science and Technology LiaoningAnshan114051China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China

This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection *** Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data *** model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT *** model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample *** data is used to retain more semantic information to generate *** model was applied to species in Southern California,USA,citing SWOT analysis data to train the *** show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development *** model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data *** study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.

关键词： Convolutional Neural Network(CNN) VAE GAN TOPSIS data reconstruction

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DNACDS:Cloud IoE big data security and accessing scheme based on DNA cryptography

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Frontiers of computer Science 2024年第1期18卷 157-170页

作者： Ashish SINGH Abhinav KUMAR Suyel NAMASUDRA School of Computer Engineering KIIT Deemed to be UniversityBhubaneshwar 751024India Department of Computer Science and Engineering Indian Institute of Information Technology SuratSurat 394190India Department of Computer Science and Engineering National Institute of Technology AgartalaAgartala 799046India

The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.

关键词： IoE based cloud computing DNA cryptography IoE big data security StS KAP feistel cipher IoE big data access

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Identify information sources with different start times in complex networks based on sparse observers

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Chinese Physics B 2024年第11期33卷 467-479页

作者： Yuan-Zhang Deng Zhao-Long Hu Feilong Lin Chang-Bing Tang Hui Wang Yi-Zhen Huang School of Computer Science and Technology Zhejiang Normal UniversityJinhua 321004China School of Physics and Electronic Information Engineering Zhejiang Normal UniversityJinhua 321004China School of Information Engineering Jinhua PolytechnicJinhua 321016China

The dissemination of information across various locations is an ubiquitous occurrence,however,prevalent methodologies for multi-source identification frequently overlook the fact that sources may initiate dissemination at distinct initial *** there are many research results of multi-source identification,the challenge of locating sources with varying initiation times using a limited subset of observational nodes remains *** this study,we provide the backward spread tree theorem and source centrality theorem,and develop a backward spread centrality algorithm to identify all the information sources that trigger the spread at different start *** proposed algorithm does not require prior knowledge of the number of sources,however,it can estimate both the initial spread moment and the spread *** core concept of this algorithm involves inferring suspected sources through source centrality theorem and locating the source from the suspected sources with linear *** experiments from synthetic and real network simulation corroborate the superiority of our method in terms of both efficacy and ***,we find that our method maintains robustness irrespective of the number of sources and the average degree of *** with classical and state-of-the art source identification methods,our method generally improves the AUROC value by 0.1 to 0.2.

关键词： complex networks information spread source identification backward spread centricity

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SmartEagleEye:A Cloud-Oriented Webshell Detection System Based on Dynamic Gray-Box and Deep Learning

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Tsinghua Science and technology 2024年第3期29卷 766-783页

作者： Xin Liu Yingli Zhang Qingchen Yu Jiajun Min Jun Shen Rui Zhou Qingguo Zhou School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computing and Information Technology University of WollongongWollongong 2500Australia

Compared with traditional environments,the cloud environment exposes online services to additional vulnerabilities and threats of cyber attacks,and the cyber security of cloud platforms is becoming increasingly prominent.A piece of code,known as a Webshell,is usually uploaded to the target servers to achieve multiple *** Webshell attacks has become a hot spot in current ***,the traditional Webshell detectors are not built for the cloud,making it highly difficult to play a defensive role in the cloud ***,a Webshell detection system based on deep learning that is successfully applied in various scenarios,is proposed in this *** system contains two important components:gray-box and neural network *** gray-box analyzer defines a series of rules and algorithms for extracting static and dynamic behaviors from the code to make the decision *** neural network analyzer transforms suspicious code into Operation Code(OPCODE)sequences,turning the detection task into a classification *** experiment results show that SmartEagleEye achieves an encouraging high detection rate and an acceptable false-positive rate,which indicate its capability to provide good protection for the cloud environment.

关键词： Webshell detection cloud web security deep learning

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Feature-Grounded Single-Stage Text-to-Image Generation

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Tsinghua Science and technology 2024年第2期29卷 469-480页

作者： Yuan Zhou Peng Wang Lei Xiang Haofeng Zhang School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing 210044China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China

Recently,Generative Adversarial Networks(GANs)have become the mainstream text-to-image(T2I)***,a standard normal distribution noise of inputs cannot provide sufficient information to synthesize an image that approaches the ground-truth image ***,the multistage generation strategy results in complex T2I ***,this study proposes a novel feature-grounded single-stage T2I model,which considers the“real”distribution learned from training images as one input and introduces a worst-case-optimized similarity measure into the loss function to enhance the model's generation *** results on two benchmark datasets demonstrate the competitive performance of the proposed model in terms of the Frechet inception distance and inception score compared to those of some classical and state-of-the-art models,showing the improved similarities among the generated image,text,and ground truth.

关键词： text-to-image(T2I) feature-grounded single-stage generation Generative Adversarial Network(GAN)

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