Highly Cited in 202509
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Cited in SCI: 56
Deep learning-based software engineering: progress, challenges, and opportunities
Chen, Xiangping; Hu, Xing; Huang, Yuan; Jiang, He; Ji, Weixing; Jiang, Yanjie; Jiang, Yanyan; Liu, Bo; Liu, Hui; Li, Xiaochen; Lian, Xiaoli; Meng, Guozhu; Peng, Xin; Sun, Hailong; Shi, Lin; Wang, Bo; Wang, Chong; Wang, Jiayi; Wang, Tiantian; Xuan, Jifeng; Xia, Xin; Yang, Yibiao; Yang, Yixin; Zhang, Li; Zhou, Yuming; Zhang, Lu
Sci China Inf Sci, 2025, 68(1): 111102
Keywords: deep learning; software engineering; software benchmark; software artifact representation; survey
Cite as: Chen X P, Hu X, Huang Y, et al. Deep learning-based software engineering: progress, challenges, and opportunities. Sci China Inf Sci, 2025, 68: 111102, doi: 10.1007/s11432-023-4127-5
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Cited in SCI: 3
A survey on large language models for software engineering
Zhang, Quanjun; Fang, Chunrong; Xie, Yang; Zhang, Yaxin; Yu, Shengcheng; Sun, Weisong; Yang, Yun; Chen, Zhenyu
Sci China Inf Sci, 2026, 69(4): 141102
Keywords: software engineering; large language model; AI and software engineering; LLM4SE
Cite as: Zhang Q J, Fang C R, Xie Y, et al. A survey on large language models for software engineering. Sci China Inf Sci, 2026, 69: 141102, doi: 10.1007/s11432-025-4670-0
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Cited in SCI: 3
From PBFT to the present: a thorough overview of blockchain consensus protocols
Feng, Liaoliao; Fu, Xiang; Wang, Huaimin; Wang, Keming; Shi, Peichang; Jiang, Feng; Lin, Moheng
Sci China Inf Sci, 2026, 69(1): 111102
Keywords: blockchain; Byzantine fault-tolerant; consensus protocol; rapid analysis framework
Cite as: Feng L L, Fu X, Wang H M, et al. From PBFT to the present: a thorough overview of blockchain consensus protocols. Sci China Inf Sci, 2026, 69: 111102, doi: 10.1007/s11432-024-4431-y
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Cited in SCI: 3
Keywords: software ingineering; infrastructure software; foundation models; executable specifications; computation offloading
Cite as: Ran D Z, Wu M Z, Cao Y, et al. An infrastructure software perspective toward computation offloading between executable specifications and foundation models. Sci China Inf Sci, 2025, 68: 146101, doi: 10.1007/s11432-025-4311-9
SCIS Selected Articles on Large Language Models (LLM)
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Cited in SCI: 2
Improving cross-task generalization with step-by-step instructions
Wu, Yang; Zhao, Yanyan; Li, Zhongyang; Qin, Bing; Xiong, Kai
Sci China Inf Sci, 2025, 68(7): 172102
Keywords: instruction tuning; generalization; step-by-step instruction; large language model; task decomposition
Cite as: Wu Y, Zhao Y Y, Li Z Y, et al. Improving cross-task generalization with step-by-step instructions. Sci China Inf Sci, 2025, 68: 172102, doi: 10.1007/s11432-023-3911-2
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Cited in SCI: 1
Effective random test generation for deep learning compilers
Ren, Luyao; Wang, Ziheng; Zhang, Li; Jiang, Guoyue; Xiong, Yingfei; Xie, Tao
Sci China Inf Sci, 2025, 68(9): 192104
Keywords: random testing; test generation; deep learning compilers; compiler testing; constraint solving
Cite as: Ren L Y, Wang Z H, Zhang L, et al. Effective random test generation for deep learning compilers. Sci China Inf Sci, 2025, 68: 192104, doi: 10.1007/s11432-023-4301-6
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Cited in SCI: 1
Learning to represent code semantics
Liu, Fang; Li, Ge; Zhao, Qianhui; Zhang, Li
Sci China Inf Sci, 2025, 68(7): 172101
Keywords: software engineering; code semantic learning; compiler intermediate representation; data dependency modeling; artificial intelligence
Cite as: Liu F, Li G, Zhao Q H, et al. Learning to represent code semantics. Sci China Inf Sci, 2025, 68: 172101, doi: 10.1007/s11432-023-3898-5
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Cited in SCI: 1
FMCC-RT: a scalable and fine-grained all-reduce algorithm for large-scale SMP clusters
Peng, Jintao; Liu, Jie; Fang, Jianbin; Xie, Min; Dai, Yi; Lai, Zhiquan; Yang, Bo; Gong, Chunye; Mao, Xinjun; Mao, Guo; Ren, Jie
Sci China Inf Sci, 2025, 68(5): 152103
Keywords: all-reduce; collective communication; MPI; scalability
Cite as: Peng J T, Liu J, Fang J B, et al. FMCC-RT: a scalable and fine-grained all-reduce algorithm for large-scale SMP clusters. Sci China Inf Sci, 2025, 68: 152103, doi: 10.1007/s11432-022-4201-7
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Cited in SCI: 1
GTE: learning code AST representation efficiently and effectively
Qin, Yihao; Wang, Shangwen; Lin, Bo; Yang, Kang; Mao, Xiaoguang
Sci China Inf Sci, 2025, 68(3): 139101
Keywords: code representation; code classification; probing techniques; deep learning; abstract syntax tree
Cite as: Qin Y H, Wang S W, Lin B, et al. GTE: learning code AST representation efficiently and effectively. Sci China Inf Sci, 2025, 68: 139101, doi: 10.1007/s11432-024-4262-5
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POSITION PAPER
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Keywords: socio-cyber-physical systems; ubiquitous computing; software engineering; software-defined everything; intelligent software
Cite as: Belbachir A, Blake M B, Dong J S, et al. Towards socio-cyber-physical systems: a software perspective amid AI breakthroughs. Sci China Inf Sci, 2026, 69: 193101, doi: 10.1007/s11432-026-5037-1
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Cited in SCI: 0
Entity-centric data management for the ubiquitous computing era
Shen, Yanyan; Wang, X. Sean; Du, Xiaoyong; Ooi, Beng Chin; Mei, Hong
Sci China Inf Sci, 2026, 69(5): 156101
Keywords: entity-centric data management; data completeness; data ownership; data accessibilty; digital twin; data valorization; ubiquitous computing
Cite as: Shen Y Y, Wang X, Du X Y, et al. Entity-centric data management for the ubiquitous computing era. Sci China Inf Sci, 2026, 69: 156101, doi: 10.1007/s11432-026-4800-x
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E-PRedictor: an approach for early prediction of pull request acceptance
Chen, Kexing; Bao, Lingfeng; Hu, Xing; Xia, Xin; Yang, Xiaohu
Sci China Inf Sci, 2025, 68(5): 152104
Keywords: pull request; prediction model; GitHub
Cite as: Chen K X, Bao L F, Hu X, et al. E-PRedictor: an approach for early prediction of pull request acceptance. Sci China Inf Sci, 2025, 68: 152104, doi: 10.1007/s11432-022-3953-4
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Characterizing the app recommendation relationships in the iOS app store: a complex network's perspective
Huang, Gang; Lin, Fuqi; Ma, Yun; Wang, Haoyu; Wang, Qingxiang; Tyson, Gareth; Liu, Xuanzhe
Sci China Inf Sci, 2025, 68(4): 142101
Keywords: mobile app; recommendation; complex network; user behavior; policy-violating app
Cite as: Huang G, Lin F Q, Ma Y, et al. Characterizing the app recommendation relationships in the iOS app store: a complex network's perspective. Sci China Inf Sci, 2025, 68: 142101, doi: 10.1007/s11432-023-3973-1
