Page 123 - 《软件学报》2025年第4期
P. 123

香佳宏 等: 大模型在软件缺陷检测与修复的应用发展综述                                                     1529


                 [190]  Jia YQ, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T. Caffe: Convolutional architecture for fast
                      feature embedding. In: Proc. of the 22nd ACM Int’l Conf. on Multimedia. Orlando: ACM, 2014. 675–678. [doi: 10.1145/2647868.
                      2654889]
                 [191]  Chen TQ, Li M, Li YT, Lin M, Wang NY, Wang MJ, Xiao TJ, Xu B, Zhang CY, Zhang Z. MXNet: A flexible and efficient machine
                      learning library for heterogeneous distributed systems. arXiv:1512.01274, 2015.
                 [192]  Wiklund K, Eldh S, Sundmark D, Lundqvist K. Impediments for software test automation: A systematic literature review. Software
                      Testing, Verification and Reliability, 2017, 27(8): e1639. [doi: 10.1002/stvr.1639]
                 [193]  Garcia SE. Usability testing: Creative techniques for answering your research questions. In: Proc. of the Extended Abstracts of the 2020
                      CHI Conf. on Human Factors in Computing Systems. Honolulu: ACM, 2020. 1–2. [doi: 10.1145/3334480.3375064]
                 [194]  Haas R, Elsner D, Juergens E, Pretschner A, Apel S. How can manual testing processes be optimized? Developer survey, optimization
                      guidelines, and case studies. In: Proc. of the 29th ACM Joint Meeting on European Software Engineering Conf. and Symp. on the
                      Foundations of Software Engineering. Athens: ACM, 2021. 1281–1291. [doi: 10.1145/3468264.3473922]
                 [195]  Petroni F, Rocktäschel T, Riedel S, Lewis P, Bakhtin A, Wu YX, Miller A. Language models as knowledge bases? In: Proc. of the 2019
                      Conf. on Empirical Methods in Natural Language Processing and the 9th Int’l Joint Conf. on Natural Language Processing (EMNLP-
                      IJCNLP). Hong Kong: Association for Computational Linguistics, 2019. 2463–2473. [doi: 10.18653/v1/D19-1250]
                 [196]  OpenAI. GPT-4 technical report. arXiv:2303.08774, 2024.
                                                  学生会员, 主
                 [197]  Yang ZY, Li LJ, Lin K, Wang JF, Lin CC, Liu ZC, Wang LJ. The dawn of LMMs: Preliminary explorations with GPT-4V(ision).
                      arXiv:2309.17421, 2023.
                 [198]  ChatGPT plugins. 2023. https://openai.com/blog/chatgpt-plugins
                 [199]  GPT-4 turbo. QpenAI help center. 2023. https://help.openai.com/en/articles/8555510-gpt-4-turbo
                 [200]  Browne R. OpenAI CEO admits a bug allowed some ChatGPT users to see others’ conversation titles. 2023. https://www.cnbc.com/
                      2023/03/23/openai-ceo-says-a-bug-allowed-some-chatgpt-to-see-others-chat-titles.html

                 附中文参考文献:
                 [31]  姜佳君, 陈俊洁, 熊英飞. 软件缺陷自动修复技术综述. 软件学报, 2021, 32(9): 2665–2690. http://www.jos.org.cn/1000-9825/6274.
                      htm [doi: 10.13328/j.cnki.jos.006274]
                 [74]  杨艺, 王嬉, 赵春蕾, 步志亮. Android GUI 自动化测试综述. 计算机科学, 2022, 49(S2): 756–765. [doi: 10.11896/jsjkx.210900231]
                 [173]  刘斌斌, 董威, 王戟. 智能化的程序搜索与构造方法综述. 软件学报, 2018, 29(8): 2180–2197. http://www.jos.org.cn/1000-9825/5529.
                      htm [doi: 10.13328/j.cnki.jos.005529]

                             香佳宏(1999-), 男, 硕士, CCF 学生会员, 主要              彭湃(1977-), 男, 本科, 主要研究领域为软件测
                            研究领域为大模型驱动的自动程序修复, 模糊                        试, 系统及软件工程.
                            测试.



                             徐霄阳(2003-), 男, 本科生, CCF  学生会员, 主             张钊(1976-), 男, 本科, 主要研究领域为系统及
                            要研究领域为大模型驱动的自动程序修复.                          软件工程, 软件测试, 研发质量管理.




                             孔繁初(2003-), 男, 本科生, CCF                      张煜群(1986-), 男, 博士, 助理教授, 博士生导
                            要研究领域为大模型驱动的自动程序修复.                          师, CCF 专业会员, 主要研究领域为模糊测试,
                                                                         软件成分分析, 污点分析, 基于大模型的软件
                                                                         工程.
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