Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (1): 236-246.doi: 10.23919/JSEE.2023.000010
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Xuefeng YAN1,2,*(), Shasha CHENG1(), Liqin GUO3()
|1||ZHOU J, ZHANG H Y, LO D Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports. Proc. of the IEEE 34th International Conference on Software Engineering, 2012, 14- 24.|
|2||HUO X, LI M, ZHOU Z H Learning unified features from natural and programming languages for locating buggy source code. Proc. of the 25th International Joint Conference on Artificial Intelligence, 2016, 1606- 1612.|
LIU Y H Optimal selection of tests for fault detection and isolation in multi-operating mode system. Journal of Systems Engineering and Electronics, 2019, 30 (2): 425- 434.
KHATIWADA S, TUSHEV M, MAHMOUD A Just enough semantics: an information theoretic approach for IR-based software bug localization. Journal of Information and Software Technology, 2018, 93, 45- 57.
YOUM K, AHN J, LEE E Improved bug localization based on code change histories and bug reports. Journal of Information and Software Technology, 2017, 82, 177- 192.
|6||YE X, SHEN H, MA X, et al From word embeddings to document similarities for improved information retrieval in software engineering. Proc. of the 38th International Conference on Software Engineering, 2016, 404- 415.|
YAN X, JACKY K, KWABENA E B, et al Improving bug localization with word embedding and enhanced convolutional neural networks. Information and Software Technology, 2019, 105, 17- 29.
|8||HUO X, LI M Enhancing the unified features to locate buggy files by exploiting the sequential nature of source code. Proc. of the 26th International Joint Conference on Artificial Intelligence, 2017, 1909- 1915.|
|9||MOU L L, LI G, ZHANG L, et al Convolutional neural networks over tree structures for programming language processing. Proc. of the 30th AAAI Conference on Artificial Intelligence, 2016, 1287- 1293.|
|10||WEI H H, LI M Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code. Proc. of the 26th International Joint Conference on Artificial Intelligence, 2017, 3034- 3040.|
|11||TAI K S, SOCHER R, MANNING C D. Improved semantic representations from tree-structured long short-term memory networks. Proc. of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 2015, 1: 1556−1566.|
|12||MOU L L, LI G, LIU Y, et al Building program vector representations for deep learning. Proc. of the International Conference on Knowledge Science, Engineering and Management, 2014, 547- 553.|
|13||JAYASUNDARA V, BUI N D Q, JIANG L, et al. TreeCaps: tree-structured capsule networks for program source code processing. https://doi.org/10.48550/arXiv.1910.12306.|
HUO X, LI M, ZHOU Z H Control flow graph embedding based on multi-instance decomposition for bug localization. Proc. of the AAAI Conference on Artificial Intelligence, 2020, 34 (4): 4223- 4230.
HU J C, CHEN J F, ZHANG L, et al A memory-related vulnerability detection approach based on vulnerability features. Journal of Tsinghua Science and Technology, 2020, 25 (5): 604- 613.
|16||WANG W H, LI G, MA B, et al Detecting code clones with graph neural network and flow-augmented abstract syntax tree. Proc. of the IEEE 27th International Conference on Software Analysis, Evolution and Reengineering, 2020, 261- 271.|
|17||SOCHER R, LIN C C, MANNING C D, et al Parsing natural scenes and natural language with recursive neural networks. Proc. of the 28th International Conference on Machine Learning, 2011, 129- 136.|
|18||HINDLE A, BARR E T, SU Z, et al On the naturalness of software. Proc. of the India Software Engineering Conference, 2012, 837- 847.|
|19||SCHRTER A, SCHROTER A, BETTENBURG N, et al Do stack traces help developers fix bugs? Proc. of the 7th IEEE Working Conference on Mining Software Repositories, 2010, 118- 121.|
|20||WONG C P, XIONG Y, ZHANG H, et al Boosting bug-report-oriented fault localization with segmentation and stack-trace analysis. Proc. of the IEEE International Conference on Software Maintenance and Evolution, 2014, 181- 190.|
|21||GHARIBI R, RASEKH A H, SADREDDINI M H Leveraging textual properties of bug reports to localize relevant source files. Information Processing & Management, 2018, 54 (6): 1058- 1076.|
|22||YE X, BUNESCU R, LIU C Learning to rank relevant files for bug reports using domain knowledge. Proc. of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering, 2014, 689- 699.|
|23||LAM A N, NGUYEN A T, NGUYEN H A Bug localization with combination of deep learning and information retrieval. Proc. of the IEEE/ACM 25th International Conference on Program Comprehension, 2017, 218- 229.|
LIANG H L, SUN L, WANG M L, et al Deep learning with customized abstract syntax tree for bug localization. IEEE Access, 2019, 7, 116309- 116320.
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