Highly Cited in 202507
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Cited in SCI: 28
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: 8
Deep learning for code generation: a survey
Zhang, Huangzhao; Zhang, Kechi; Li, Zhuo; Li, Jia; Li, Yongmin; Zhao, Yunfei; Zhu, Yuqi; Liu, Fang; Li, Ge; Jin, Zhi
Sci China Inf Sci, 2024, 67(9): 191101
Keywords: code generation; automated software engineering; deep learning; large model; artificial intelligence
Cite as: Zhang H Z, Zhang K C, Li Z, et al. Deep learning for code generation: a survey. Sci China Inf Sci, 2024, 67: 191101, doi: 10.1007/s11432-023-3956-3
From CAS & CAE Members
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Cited in SCI: 5
Categorizing methods for integrating machine learning with executable specifications
Harel, David; Yerushalmi, Raz; Marron, Assaf; Elyasaf, Achiya
Sci China Inf Sci, 2024, 67(1): 111101
Keywords: machine learning; artificial intelligence; grey box learning; domain knowledge; rules; behavioral programming; deep reinforcement learning; survey
Cite as: Harel D, Yerushalmi R, Marron A, et al. Categorizing methods for integrating machine learning with executable specifications. Sci China Inf Sci, 2024, 67: 111101, doi: 10.1007/s11432-022-3826-6
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Cited in SCI: 2
Keywords: functionality prediction; incomplete programming code; syntactical information; code representation model; deep learning algorithm
Cite as: Yu Y S, Huang Z Q, Shen G H, et al. ASTSDL: predicting the functionality of incomplete programming code via an AST-sequence-based deep learning model. Sci China Inf Sci, 2024, 67: 112105, doi: 10.1007/s11432-021-3665-1
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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
SCIS Selected Articles on Large Language Models (LLM)
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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
PERSPECTIVE
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Cited in SCI: 1
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
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Cited in SCI: 1
What can we learn from quality assurance badges in open-source software?
Li, Feng; Lou, Yiling; Tan, Xin; Chen, Zhenpeng; Dong, Jinhao; Li, Yang; Wang, Xuanzhi; Hao, Dan; Zhang, Lu
Sci China Inf Sci, 2024, 67(4): 142103
Keywords: quality assurance; badge; open-source software; code quality; empirical study
Cite as: Li F, Lou Y L, Tan X, et al. What can we learn from quality assurance badges in open-source software?. Sci China Inf Sci, 2024, 67: 142103, doi: 10.1007/s11432-022-3611-3
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Cited in SCI: 0
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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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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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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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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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
LETTER
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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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An efficient schedulability analysis based on worst-case interference time for real-time systems
Liu, Hongbiao; Yang, Mengfei; Qiao, Lei; Chen, Xi; Gong, Jian
Sci China Inf Sci, 2024, 67(9): 192103
Keywords: five-tuple real-time task model; real-time system; spacecraft; Internet of Things; exact schedulability analysis; worst-case interference time
Cite as: Liu H B, Yang M F, Qiao L, et al. An efficient schedulability analysis based on worst-case interference time for real-time systems. Sci China Inf Sci, 2024, 67: 192103, doi: 10.1007/s11432-022-3891-4
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Keywords: open source software; open source community; community response; contributor retention; survival analysis
Cite as: Wang Z, Li Z X, Yu Y. Do community responses influence OSS contributor retention? A survival analysis. Sci China Inf Sci, 2024, 67: 184101, doi: 10.1007/s11432-024-4090-0
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Improving actionable warning identification via the refined warning-inducing context representation
Ge, Xiuting; Fang, Chunrong; Li, Xuanye; Zhang, Quanjun; Liu, Jia; Zhao, Zhihong; Chen, Zhenyu
Sci China Inf Sci, 2024, 67(5): 159101
Keywords: Actionable warning identification; Warning representation; Program slicing; Static analysis; Machine learning
Cite as: Ge X T, Fang C R, Li X Y, et al. Improving actionable warning identification via the refined warning-inducing context representation. Sci China Inf Sci, 2024, 67: 159101, doi: 10.1007/s11432-023-3975-6
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Keywords: software debugging; fault localization; program repair; artificial intelligence; machine learning
Cite as: Song Y, Xie X Y, Xu B W. When debugging encounters artificial intelligence: state of the art and open challenges. Sci China Inf Sci, 2024, 67: 141101, doi: 10.1007/s11432-022-3803-9
