[1] BROOKS J R, FREDERICK P. The mythical man-month[M]. Essays on Softw(1st.ed.). Addison-wesley Longman Publishing C, Inc., USA, 1978.
[2] LAWRIE D, FEILD H, BINKLEY D. An empirical study of rules for well-formed identifiers[J]. Journal of Software Maintenance and Evolution: Research and practice, 2007, 19(4): 205-229.
[3] BINKLEY D, DAVIS M, LAWRIE D, et al. The impact of identifier style on effort and comprehension[J]. Empirical Software Engineering, 2013, 18(2): 219-276.
[4] JIANG Y, LIU H, ZHU J, et al. Automatic and accurate expansion of abbreviations in parameters[J]. IEEE Transactions on Software Engineering, 2018, 46(7): 732-747.
[5] CARTER B. On choosing identifiers[J]. ACM Sigplan Notices, 1982, 17(5): 54-59.
[6] TU Z, SU Z, DEVANBU P. On the localness of software[C]//Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering, 2014: 269-280.
[7] HINDLE A, BARR E T, GABEL M, et al. On the naturalness of software[J]. Communications of the ACM, 2016, 59(5): 122-131.
[8] 张静宣, 江贺. 代码标识符归一化研究现状及发展趋势[J]. 计算机科学, 2020,47(3): 1-4.
[9] ALON U, ZILBERSTEIN M, LEVY O, et al. Code2vec: Learning distributed representations of code[C]//Proceedings of the ACM on Programming Languages, 2019, 3(POPL): 1-29.
[10] KARAMPATSIS R M, SUTTON C. Scelmo: Source code embeddings from language models[J]. arXiv preprint arXiv: 2004.13214, 2020.
[11] FENG Z, GUO D, TANG D, et al. CodeBERT: A pre-trained model for programming and natural languages[J]. arXiv preprint arXiv: 2002.08155, 2020.
[12] HUSAIN H, WU H H, GAZIT T, et al. Codesearchnet challenge: Evaluating the state of semantic code search[J]. arXiv preprint arXiv: 1909.09436, 2019.
[13] GU X, ZHANG H, KIM S. Deep code search[C]//Proceedings of the IEEE/ACM 40th International Conference on Software Engineering. IEEE, 2018: 933-944.
[14] ZHU Y, PAN M. Automatic code summarization: A systematic literature review[J]. arXiv preprint arXiv: 1909.04352, 2019.
[15] LAWRIE D, BINKLEY D. On the value of bug reports for retrieval-based bug localization[C]//Proceedings of the IEEE International Conference on Software Maintenance and Evolution. IEEE, 2018: 524-528.
[16] LI X, JIANG H, LIU D, et al. Unsupervised deep bug report summarization[C]//Proceedings of the IEEE/ACM 26th International Conference on Program Comprehension. IEEE, 2018: 144-14411.
[17] RASTKAR S, MURPHY G C, MURRAY G. Automatic summarization of bug reports[J]. IEEE Transactions on Software Engineering, 2014, 40(4): 366-380.
[18] AGGARWAL K K, SINGH Y, CHHABRA J K. An integrated measure of software maintainability[C]//Proceedings of the Annual Reliability and Maintainability Symposium. Proceedings. IEEE, 2002: 235-241.
[19] BUTLER S, WERMELINGER M, YU Y, et al. Exploring the influence of identifier names on code quality: An empirical study[C]//Proceedings of the 14th European Conference on Software Maintenance and Reengineering. IEEE, 2010: 156-165.
[20] LIBLIT B, BEGEL A, SWEETSER E. Cognitive perspectives on the role of naming in computer programs[C]//Proceedings of the PPIG, 2006.
[21] EBAD S A, MANZOOR D. An empirical comparison of Java and C# programs in following naming conventions[J]. International Journal of People-Oriented Programming, 2016, 5(1): 39-60.
[22] BRSTLER J, CASPERSEN M E, NORDSTRM M. Beauty and the beast: On the readability of object-oriented example programs[J]. Software Quality Journal, 2016, 24(2): 231-246.
[23] BUSE R P L, WEIMER W R. Learning a metric for code readability[J]. IEEE Transactions on Software Engineering, 2009, 36(4): 546-558.
[24] PAWELKA T, JUERGENS E. Is this code written in English?: A study of the natural language of comments and identifiers in practice[C]//Proceedings of the IEEE International Conference on Software Maintenance and Evolution. IEEE, 2015: 401-410.
[25] CARVALHO N R, ALMEIDA J J, HENRIQUES P R, et al. From source code identifiers to natural language terms[J]. Journal of Systems and Software, 2015, 100: 117-128.
[26] WANG Y, WANG C, LI X, et al. How are identifiers named in open source software? About popularity and consistency[J]. arXiv preprint arXiv: 1401.5300, 2014.
[27] PIRAPURAJ P, PERERA I. Analyzing source code identifiers for code reuse using NLP techniques and WordNet[C]//Proceedings of the Moratuwa Engineering Research Conference. IEEE, 2017: 105-110.
[28] ARNAOUDOVA V, ESHKEVARI L M, DI P M, et al. Repent: Analyzing the nature of identifier renamings[J]. IEEE Transactions on Software Engineering, 2014, 40(5): 502-532.
[29] DEVLIN J, CHANG M W, LEE K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding[J]. arXiv preprint arXiv: 1810.04805, 2018.
[30] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[J]. arXiv preprint arXiv: 1301.3781, 2013.
[31] PETERS M E, NEUMANN M, Iyyer M, et al. Deep contextualized word representations[J]. arXiv preprint arXiv: 1802.05365, 2018.
[32] 聂黎明,江贺,高国军,等.代码搜索与API推荐文献分析[J].计算机科学,2017,44(S1): 475-482.
[33] GUO D, REN S, LU S, et al. GraphcodeBERT: Pre-training code representations with data flow[C]//Proceedings of the 9th International Conference on Learning Representations, 2021.
[34] CHEN M, TWOREK J, JUN H, et al. Evaluating large language models trained on code[J]. arXiv preprint arXiv: 2107.03374, 2021.
[35] LIU C, XIA X, LO D, et al. Opportunities and challenges in code search tools[J]. arXiv preprint arXiv: 2011.02297, 2020.
[36] LE T H M, CHEN H, BABAR M A. Deep learning for source code modeling and generation: Models, applications, and challenges[J]. ACM Computing Surveys, 2020, 53(3): 1-38.
[37] CAMBRONERO J, LI H, KIM S, et al. When deep learning met code search[C]//Proceedings of the 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2019: 964-974.
[38] SUEN C Y. N-gram statistics for natural language understanding and text processing[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1979 (2): 164-172.
[39] PENNINGTON J, SOCHER R, MANNING C D. Glove: Global vectors for word representation[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2014: 1532-1543.
[40] LIU Y, OTT M, GOYAL N, et al. RoBERTa: A robustly optimized bert pretraining approach[J]. arXiv preprint arXiv: 1907.11692, 2019.
[41] WHITE M, TUFANO M, VENDOME C, et al. Deep learning code fragments for code clone detection[C]//Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering. IEEE, 2016: 87-98.
[42] 运思婧. 基于词性规则的软件标识符质量评价方法[D].哈尔滨: 哈尔滨工业大学硕士学位论文,2011.
[43] MCCABE T J. A complexity measure[J]. IEEE Transactions on Software Engineering, 1976,4: 308-320.
[44] CHEN M, TWOREK J, JUN H, et al. Evaluating large language models trained on code[J]. arXiv preprint arXiv: 2107.03374, 2021.