Page 250 - 《软件学报》2026年第5期

张添翼 等: LLM-Extractor: 基于大语言模型的软件配置间约束提取方法 2129

datacenter systems. In: Proc. of the 28th ACM Joint Meeting on European Software Engineering Conf. and Symp. on the Foundations of
Software Engineering. ACM, 2020. 362–374. [doi: 10.1145/3368089.3409727]
[14] Zhang JL, Piskac R, Zhai EN, Xu TY. Static detection of silent misconfigurations with deep interaction analysis. Proc. of the ACM on
Programming Languages, 2021, 5(OOPSLA): 140. [doi: 10.1145/3485517]
[15] Rabkin A, Katz R. Static extraction of program configuration options. In: Proc. of the 33rd Int’l Conf. on Software Engineering.
Honolulu: IEEE, 2011. 131–140. [doi: 10.1145/1985793.1985812]
[16] Dong Z, Andrzejak A, Lo D, Costa D. ORPLocator: Identifying read points of configuration options via static analysis. In: Proc. of the
27th IEEE Int’l Symp. on Software Reliability Engineering. Ottawa: IEEE, 2016. 185–195. [doi: 10.1109/ISSRE.2016.37]
[17] Zhou SL, Liu XD, Li SS, Dong W, Liao XK, Xiong Y. ConfMapper: Automated variable finding for configuration items in source code.
In: Proc. of the 2016 IEEE Int’l Conf. on Software Quality, Reliability and Security Companion. Vienna: IEEE, 2016. 228–235. [doi: 10.
1109/QRS-C.2016.35]
[18] Zhang JQ, Renganarayana L, Zhang XL, Ge NY, Bala V, Xu TY, Zhou YY. EnCore: Exploiting system environment and correlation
information for misconfiguration detection. In: Proc. of the 19th Int’l Conf. on Architectural Support for Programming Languages and
Operating Systems. Salt Lake City: ACM, 2014. 687–700. [doi: 10.1145/2541940.2541983]
[19] Chen W, Qiao XQ, Wei J, Zhong H, Huang X. Detecting inter-component configuration errors in proactive: A relation-aware method. In:
Proc. of the 14th Int’l Conf. on Quality Software. Allen: IEEE, 2014. 184–189. [doi: 10.1109/QSIC.2014.37]
[20] Santolucito M, Zhai EN, Piskac R. Probabilistic automated language learning for configuration files. In: Proc. of the 28th Int’l Conf. of
Computer Aided Verification. Toronto: Springer, 2016. 80–87. [doi: 10.1007/978-3-319-41540-6_5]
[21] Santolucito M, Zhai EN, Dhodapkar R, Shim A, Piskac R. Synthesizing configuration file specifications with association rule learning.
Proc. of the ACM on Programming Languages, 2017, 1(OOPSLA): 64. [doi: 10.1145/3133888]
[22] Bhagwan R, Mehta S, Radhakrishna A, Garg S. Learning patterns in configuration. In: Proc. of the 36th IEEE/ACM Int’l Conf. on
Automated Software Engineering. Melbourne: IEEE, 2021. 817–828. [doi: 10.1109/ASE51524.2021.9678525]
[23] Mehta S, Bhagwan R, Kumar R, Bansal C, Maddila C, Ashok B, Asthana S, Bird C, Kumar A. Rex: Preventing bugs and
misconfiguration in large services using correlated change analysis. In: Proc. of the 17th USENIX Symp. on Networked Systems Design
and Implementation. Santa Clara: USENIX Association, 2020. 435–448.
[24] Tuncer O, Bila N, Duri S, Isci C, Coskun AK. ConfEx: Towards automating software configuration analytics in the cloud. In: Proc. of the
48th Annual IEEE/IFIP Int’l Conf. on Dependable Systems and Networks Workshops. Luxembourg: IEEE, 2018. 30–33. [doi: 10.1109/
DSN-W.2018.00019]
[25] Xu XY, Li SS, Guo Y, Dong W, Li W, Liao XK. Automatic type inference for proactive misconfiguration prevention. In: Proc. of the
29th Int’l Conf. on Software Engineering and Knowledge Engineering. Pittsburgh: ksiresearch.org, 2017. 295–300. [doi: 10.18293/
SEKE2017-072]
[26] Xiang CC, Huang HC, Yoo A, Zhou YY, Pasupathy S. PracExtractor: Extracting configuration good practices from manuals to detect
server misconfigurations. In: Proc. of the 2020 USENIX Annual Technical Conf. USENIX Association, 2020. 265–280.
[27] Zhou SL, Liu XD, Li SS, Jia ZY, Zhang YL, Wang T, Li W, Liao XK. ConfInLog: Leveraging software logs to infer configuration
constraints. In: Proc. of the 29th IEEE/ACM Int’l Conf. on Program Comprehension. Madrid: IEEE, 2021. 94–105. [doi: 10.1109/
ICPC52881.2021.00018]
[28] Mandal S, Chethan A, Janfaza V, Mahmud SMF, Anderson TA, Turek J, Tithi JJ, Muzahid A. Large language models based automatic
synthesis of software specifications. arXiv:2304.09181, 2023.
[29] Xiao CR, Xu SX, Zhang KP, Wang YF, Xia L. Evaluating reading comprehension exercises generated by LLMs: A showcase of
ChatGPT in education applications. In: Proc. of the 18th Workshop on Innovative Use of NLP for Building Educational Applications
(BEA 2023). Toronto: ACL, 2023. 610–625. [doi: 10.18653/v1/2023.bea-1.52]
[30] Peng Y, Wang CZ, Wang WX, Gao CY, Lyu MR. Generative type inference for Python. arXiv:2307.09163, 2023.
[31] Zhao WX, Zhou K, Li JY, et al. A survey of large language models. arXiv:2303.18223, 2025.
[32] Brown TB, Mann B, Ryder N, et al. Language models are few-shot learners. In: Proc. of the 34th Int’l Conf. on Neural Information
Processing Systems. Vancouver: Curran Associates Inc., 2020. 1877–1901.
[33] Touvron H, Lavril T, Izacard G, et al. LLaMA: Open and efficient foundation language models. arXiv:2302.13971, 2023.
[34] Google. Gemini. 2024. https://gemini.google.com/
[35] 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: ACL, 2019. 2463–2473. [doi: 10.18653/v1/D19-1250]