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

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Science China(Information Sciences) 2025年

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.

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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

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.

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AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

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computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China state key laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of computer Science and technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

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computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi Department of computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of computer Science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements Engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

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An Uncertainty Enhanced Trust Evolution Strategy for e-Science

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Journal of computer Science & technology 2010年第6期25卷 1225-1236页

作者：杜薇崔国华刘伟 School of computer Science and technology Huazhong University of Science and Technology College of computer Science and technology Wuhan University of Technology state key lab of software Engineering Wuhan University state key laboratory for Novel software technology Nanjing University

Resources shared in e-Science have critical requirements on *** subjective trust management is essential to guarantee users＇ collaborations and communications on such a promising *** an important nature of subjective trust,uncertainty should be preserved and exhibited in trust definition,representation and *** the drawbacks of existing mechanisms based on random mathematics and fuzzy theory,this paper designs an uncertainty enhanced trust evolution strategy based on cloud model *** define subjective trust as trust *** we propose new algorithms to propagate,aggregate and update ***,based on the concept of similar cloud,a method to assess trust level is put *** simulation results show the effiectiveness,rationality and efficiency of our proposed strategy.

关键词： cloud model theory similar cloud operator subjective trust trust cloud

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Adaptive Multi-layer Particle Swarm Optimization with Neighborhood Search

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Chinese Journal of Electronics 2016年第6期25卷 1079-1088页

作者： TRAN Dang Cong WU Zhijian state key lab of software Engineering School of ComputerWuhan University Vietnam Academy of Science and technology

Particle swarm optimization(PSO) has shown a good performance on solving global optimization problems. Traditional PSO has two main drawbacks of premature convergence and low convergence speed, especially on complex problems. This paper presents a new approach called Adaptive multi-layer particle swarm optimization with neighborhood search(AMPSONS), where the traditional PSO is improved by employing an adaptive multi-layer search and neighborhood search strategy to achieve a trade-off between exploitation and exploration abilities. In order to evaluate the performance of the proposed AMPSONS algorithm, the performance of AMPSONS is compared with five other PSO family algorithms,namely, CLPSO, DNLPSO, DNSPSO, global MLPSO and local MLPSO on a set of benchmark functions. The comparison results show that AMPSONS has a promising performance on ma jority of the test functions.

关键词： Particle swarm optimization Global optimization Neighborhood search Adaptive multi-layer search

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Reflectance edge guided networks for detail-preserving intrinsic image decomposition

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Science China(Information Sciences) 2023年第2期66卷 142-157页

作者： Quewei LI Jie GUO Zhengyi WU Yang FEI Yanwen GUO state key lab for Novel software technology Nanjing University

Deep learning-based intrinsic image decomposition methods rely heavily on large-scale training data. However, current real-world datasets only contain sparse annotations, leading to textureless reflectance estimation. Although densely-labeled synthetic datasets are available, the large bias between these two categories easily incurs noticeable artifacts(e.g., shading residuals) on reflectance. To address this issue,we introduce reflectance edges that are predicted by a neural network trained on synthetic data with full supervision. Once trained, this network is able to capture high-frequency details of reflectance while greatly reducing the bias stemming from the discrepancy between different data distributions. We design another neural network to remove shading as much as possible from the input image. As this network is trained solely on real-world datasets, little bias will be introduced but the predicted reflectance will be overly smooth due to limited annotations. To recover texture details of the reflectance while still suppressing bias, we leverage a third neural network to progressively fuse feature maps from both reflectance edge maps and coarse-grained reflectance maps. The well-designed fusion strategy makes the best use of features extracted from the realworld data and helps to generate texture-rich reflectance with fewer artifacts. Extensive experiments on multiple benchmark datasets demonstrate the superiority of the proposed method.

关键词： intrinsic image decomposition detail-preserving reflectance edges

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Deep graph learning for spatially-varying indoor lighting prediction

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Science China(Information Sciences) 2023年第3期66卷 173-187页

作者： Jiayang BAI Jie GUO Chenchen WANG Zhenyu CHEN Zhen HE Shan YANG Piaopiao YU Yan ZHANG Yanwen GUO state key lab for Novel software technology Nanjing University

Lighting prediction from a single image is becoming increasingly important in many vision and augmented reality(AR) applications in which shading and shadow consistency between virtual and real objects should be guaranteed. However, this is a notoriously ill-posed problem, especially for indoor scenarios,because of the complexity of indoor luminaires and the limited information involved in 2D images. In this paper, we propose a graph learning-based framework for indoor lighting estimation. The core is a new lighting model(DSGLight) based on depth-augmented spherical Gaussians(SGs) and a graph convolutional network(GCN) that infers the new lighting representation from a single low dynamic range(LDR) image of limited field-of-view. Our lighting model builds 128 evenly distributed SGs over the indoor panorama, where each SG encodes the lighting and the depth around that node. The proposed GCN then learns the mapping from the input image to DSGLight. Compared with existing lighting models, our DSGLight encodes both direct lighting and indirect environmental lighting more faithfully and compactly. It also makes network training and inference more stable. The estimated depth distribution enables temporally stable shading and shadows under spatially-varying lighting. Through thorough experiments, we show that our method obviously outperforms existing methods both qualitatively and quantitatively.

关键词： lighting graph learning augmented reality spherical Gaussians rendering

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LPW: an efficient data-aware cache replacement strategy for Apache Spark

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Science China(Information Sciences) 2023年第1期66卷 77-96页

作者： Hui LI Shuping JI Hua ZHONG Wei WANG Lijie XU Zhen TANG Jun WEI Tao HUANG state key lab of computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Nanjing Institute of software technology

Caching is one of the most important techniques for the popular distributed big data processing framework Spark. For this big data parallel computing framework, which is designed to support various applications based on in-memory computing, it is not possible to cache every intermediate result due to the memory size limitation. The arbitrariness of cache application programming interface(API) usage,the diversity of application characteristics, and the variability of memory resources constitute challenges to achieving high system execution performance. Inefficient cache replacement strategies may cause different performance problems such as long application execution time, low memory utilization, high replacement frequency, and even program execution failure resulting from out of memory. The cache replacement strategy currently adopted by Spark is the least recently used(LRU) strategy. Although LRU is a classical algorithm and has been widely used, it lacks consideration for the environment and workloads. As a result, it cannot achieve good performance under many scenarios. In this paper, we propose a novel cache replacement algorithm, least partition weight(LPW). LPW takes comprehensive consideration of different factors affecting system performance, such as partition size, computational cost, and reference count. The LPW algorithm was implemented in Spark and compared against the LRU as well as other state-of-the-art mechanisms. Our detailed experiments indicate that LPW obviously outperforms its counterparts and can reduce the execution time by up to 75% under typical workloads. Furthermore, the decreasing eviction frequency also shows the LPW algorithm can generate more reasonable predictions.

关键词： Spark memory cache replacement least partition weight data-aware

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