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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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AsyCo: an asymmetric dual-task co-training model for partial-label learning

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Science China(Information Sciences) 2025年第5期68卷 332-347页

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.

关键词： machine learning weakly supervised learning partial-label learning co-training models candidate label sets

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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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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 scalability 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 scalability,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 scalability 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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Identifying Performance Inefficiencies of Parallel Program with Spatial and Temporal Trace Analysis

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IEEE Transactions on Parallel and Distributed Systems 2025年第7期36卷 1387-1400页

作者： Xuan, Zhibo Sun, Xin You, Xin Yang, Hailong Luan, Zhongzhi Liu, Yi Qian, Depei Beihang University State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beijing100191 China

Performance inefficiencies can lead to performance anomalies in parallel programs. Existing performance analysis tools either have a limited detection scope or require significant domain knowledge to use, which constrains their practical adoption to identify performance inefficiencies. In this paper, we propose STAD, a performance analysis tool for parallel programs that considers both spatial and temporal patterns within trace data. STAD captures the spatial communication patterns between processes using a spatial communication pattern graph. It then adopts a dynamic graph neural network-based unsupervised model to learn the evolving temporal patterns along the timeline. Additionally, STAD diagnoses the root causes of performance anomalies by exploiting the aggregated feature of anomalies along the call tree. Our evaluation results demonstrate that STAD can effectively detect performance anomalies with acceptable overhead and diagnose the root causes attributed to both the program itself and the running environment. © 1990-2012 IEEE.

关键词： Performance Analysis Training Libraries Graph Neural Networks Analytical Models Accuracy Sun Phasor Measurement Units Heating Systems Hardware Anomalies Parallel Programs Performance Analysis Root Cause Parallel Computing Spatial Patterns Communication Patterns Trace Data Programming Tools Dynamic Graph Artificial Neural Network Matrix Elements Unsupervised Learning Unique Way Anomaly Detection Red Points Graph Convolutional Network System Noise Variational Autoencoder Graph Neural Networks Program Execution Time Slice Network Congestion Node Score Anomaly Score Dynamic Topology Graph Attention Network Dynamic Neural Network Load Imbalance White Blocks Dynamic Network Graph Neural Network Model Tree View Vector Of Node

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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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ICCG:low-cost and efficient consistency with adaptive synchronization for metadata replication

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Frontiers of computer Science 2025年第1期19卷 53-70页

作者： Chenhao ZHANG Liang WANG Jing SHANG Zhiwen XIAO Limin XIAO Meng HAN Bing WEI Runnan SHEN Jinquan WANG State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Cyberspace Security Hainan UniversityHaikou 570228China China Mobile Information Technology Center Beijing 100033China

The rapid growth in the storage scale of wide-area distributed file systems (DFS) calls for fast and scalable metadata management. Metadata replication is the widely used technique for improving the performance and scalability of metadata management. Because of the POSIX requirement of file systems, many existing metadata management techniques utilize a costly design for the sake of metadata consistency, leading to unacceptable performance overhead. We propose a new metadata consistency maintenance method (ICCG), which includes an incremental consistency guaranteed directory tree synchronization (ICGDT) and a causal consistency guaranteed replica index synchronization (CCGRI), to ensure system performance without sacrificing metadata consistency. ICGDT uses a flexible consistency scheme based on the state of files and directories maintained through the conflict state tree to provide an incremental consistency for metadata, which satisfies both metadata consistency and performance requirements. CCGRI ensures low latency and consistent access to data by establishing a causal consistency for replica indexes through multi-version extent trees and logical time. Experimental results demonstrate the effectiveness of our methods. Compared with the strong consistency policies widely used in modern DFSes, our methods significantly improve the system performance. For example, in file creation, ICCG can improve the performance of directory tree operations by at least 36.4 times.

关键词： metadata management metadata replication consistency directory tree replica index

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Characterizing the app recommendation relationships in the iOS app store: a complex network's perspective

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Science China(Information Sciences) 2025年第4期68卷 284-299页

作者： Gang HUANG Fuqi LIN Yun MA Haoyu WANG Qingxiang WANG Gareth TYSON Xuanzhe LIU Key Lab of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University Institute for Artificial Intelligence Peking University School of Cyber Science and Engineering Huazhong University of Science and Technology Alibaba Group Hong Kong University of Science and Technology (Guangzhou)

Mobile apps have become widely adopted in our daily lives. To facilitate app discovery, most app markets provide recommendations for users, which may significantly impact how apps are accessed. However, little has been known about the underlying relationships and how they reflect(or affect) user behaviors. To fill this gap, we characterize the app recommendation relationships in the i OS app store from the perspective of the complex network. We collect a dataset containing over 1.3 million apps and 50 million app recommendations. This dataset enables us to construct a complex network that captures app recommendation relationships. Through this, we explore the recommendation relationships between mobile apps and how these relationships reflect or affect user behavior patterns. The insights gained from our research can be valuable for understanding typical user behaviors and identifying potential policy-violating apps.

关键词： mobile app recommendation complex network user behavior policy-violating app

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PathActMarker:an R package for inferring pathway activity of complex diseases

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Frontiers of computer Science 2025年第3期19卷 123-124页

作者： Xingyi LI Jun HAO Zhelin ZHAO Junming LI Xingyu LIAO Min LI Xuequn SHANG School of Computer Science Northwestern Polytechnical UniversityXi’an 710072China Research&Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518063China School of Software Northwestern Polytechnical UniversityXi’an 710072China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China

1 Introduction The process of complex diseases is closely linked to the disruption of key biological pathways,it is crucial to identify the dysfunctional pathways and quantify the degree of dysregulation at the indivi... 详细信息

关键词： pathway activity dysregulation complex diseases biological pathways disruption key biological pathwaysit identify dysfunctional pathways

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Layout Decomposition via Boolean Satisfiability

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 1112-1125页

作者： Liu, Hongduo Liao, Peiyu Zou, Mengchuan Pang, Bowen Li, Xijun Yuan, Mingxuan Ho, Tsung-Yi Yu, Bei The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong Huawei Noah's Ark Lab Hong Kong Hong Kong Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science Beijing100190 China

Multiple patterning lithography (MPL) has been introduced in the integrated circuits manufacturing industry to enhance feature density as the technology node advances. A crucial step of MPL is assigning layout features to different masks, namely layout decomposition. Exact algorithms like integer linear programming (ILP) can solve layout decomposition to optimality but lack scalability for dense patterns. Relaxation algorithms (e.g., linear programming and semi-definite programming) and heuristics (e.g., exact cover) are capable of handling large cases at the cost of inferior solution quality. These methods rely on different mathematical solvers and expert-designed heuristics to offer a balance between solution quality and computational efficiency. In this article, we propose a unified layout decomposition framework comprising three algorithms: 1) satisfiability (SAT)-exact;2) SAT-bilevel;and 3) SAT-fast, all leveraging the capabilities of Boolean SAT solvers. The SAT-exact ensures optimality, but with faster convergence than ILP, SAT-bilevel addresses the decomposition as a bilevel optimization problem for rapid near-optimal solutions, and SAT-fast handles very large layouts in an incremental manner. Experimental results demonstrate our framework's superiority over existing state-of-the-art methods in terms of solution quality and runtime. © 2024 IEEE.

关键词： Integer linear programming

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