The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Evaluating Large Language Models in Class-Level Code Generation
Open accessAbstract
Recently, many large language models (LLMs) have been proposed, showing advanced proficiency in code generation. Meanwhile, many efforts have been dedicated to evaluating LLMs on code generation benchmarks such as HumanEval. Although being very helpful for comparing different LLMs, existing evaluation focuses on a simple code generation scenario (i.e., function-level or statement-level code generation), which mainly asks LLMs to generate one single code unit (e.g., a function or a statement) for the given natural language description. Such evaluation focuses on generating independent and often small-scale code units, thus leaving it unclear how LLMs perform in real-world software development scenarios.
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Citations
Cites
LLaMA: Open and Efficient Foundation Language Models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.13971 · Cited by 4,602
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E. P., et al. (2023) · arXiv · doi:10.48550/arxiv.2306.05685 · Cited by 1,888
Lost in the Middle: How Language Models Use Long Contexts
Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., & Liang, P. (2023) · arXiv · doi:10.48550/arxiv.2307.03172 · Cited by 1,599
Competition-level code generation with AlphaCode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al. (2022) · Science · doi:10.1126/science.abq1158 · Cited by 1,043
Competition-Level Code Generation with AlphaCode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Lago, A. D., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.07814 · Cited by 1,043
Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al. (2023) · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.ijcnlp-main.45 · Cited by 682
Discrete Signal Processing on Graphs: Sampling Theory
Chen, S., Varma, R., Sandryhaila, A., & Kovacevic, J. (2015) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2015.2469645 · Cited by 666
A systematic evaluation of large language models of code
Xu, F. F., Alon, U., Neubig, G., & Hellendoorn, V. J. (2022) · Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming · doi:10.1145/3520312.3534862 · Cited by 540
Recommender Systems in the Era of Large Language Models (LLMs)
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“We select the state-of-the-art LLMs that have been widely studied in recent code generation work [44, 49].”
BackgroundMethod
Cited by
A Survey on Large Language Models for Code Generation
Jiang, J., Wang, F., Shen, J., Kim, S., & Kim, S. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3747588 · Cited by 251
The future of AI-driven software engineering
Terragni, V., Vella, A., Roop, P., & Blincoe, K. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3715003 · Cited by 62
Teaching Code LLMs to Use Autocompletion Tools in Repository-Level Code Generation
Wang, C., Zhang, J., Feng, Y., Li, T., Sun, W., Liu, Y., & Peng, X. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3714462 · Cited by 40
“Recent advancements have introduced a variety of code large language models (LLMs) [7, 12, 13, 15, 17, 21, 28, 29, 33, 37, 46, 53, 54] constructed upon the Transformer model architecture [45], achieving promising performance in code-related applications [19, 20, 47—50, 52, 57, 59, 60].”
Self-citationPractitioners’ expectations on log anomaly detection
Ma, X., Li, Y., Keung, J., Yu, X., Zou, H., Yang, Z., Sarro, F., & Barr, E. T. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3586700 · Cited by 21
“…of LLMs to aid in the development of log anomaly detection techniques, given their increasing popularity for software engineering tasks [68], [69], [70], particularly in log analysis-related activities, such as employing zero-shot learning for log parsing [71] and integrating inter-pretable…”
CP-Agent: Agentic Constraint Programming
Szeider, S. (2025) · arXiv · doi:10.48550/arxiv.2508.07468 · Cited by 1
Metadata
- Authors
- Du, X., Liu, M., Wang, K., Wang, H., Liu, J., Chen, Y., Feng, J., Sha, C., Peng, X., & Lou, Y.
- Venue
- Proceedings of the IEEE/ACM 46th International Conference on Software Engineering
- Publisher
- ACM
- Published
- 2024-04-12
- Pages
- 1-13
- OpenAlex
- W4394745858
- Cited by
- 162
- References
- 21
- FWCI
- 63.67
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Du, X., Liu, M., Wang, K., Wang, H., Liu, J., Chen, Y., Feng, J., Sha, C., Peng, X., & Lou, Y. (2024). Evaluating Large Language Models in Class-Level Code Generation. Proceedings of the IEEE/ACM 46th International Conference on Software Engineering. https://doi.org/10.1145/3597503.3639219
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