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With the advancements of Large Language Models (LLMs), an increasing number of open-source software projects are using LLMs as their core functional component.
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Frantar, E., Alistarh, D., 2023 · 2023
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Prompt injection attack against llm-integrated applications
Liu, Y., Deng, G., Li, Y., Wang, K., Wang, Z., Wang, X., Zhang, T., Liu, Y., Wang, H., Zheng, Y., et al., 2023 · 2023
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Understanding the issues, their causes and solutions in microservices systems: An empirical study
Waseem, M., Liang, P., Ahmad, A., Khan, A.A., Shahin, M., Abrahamsson, P., Nasab, A.R., Mikkonen, T., 2023 · 2023
Cited alongside, same era.
What do users ask in open-source ai repositories? an empirical study of github issues, in: Proceedings of the 20th International Conference on Mining Software Repositories (MSR), IEEE. pp. 79–91
Yang, Z., Wang, C., Shi, J., Hoang, T., Kochhar, P., Lu, Q., Xing, Z., Lo, D., 2023 · 2023
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Towards an understanding of large language models in software engineering tasks
Zheng, Z., Ning, K., Chen, J., Wang, Y., Chen, W., Guo, L., Wang, W., 2023 · 2023
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How do hugging face models document datasets, bias, and licenses? an empirical study, in: Proceedings of the 32nd IEEE/ACM International Conference on Program Comprehension (ICPC), ACM. pp. 370–381
Pepe, F., Nardone, V., Mastropaolo, A., Canfora, G., Bavota, G., Di Penta, M., 2024 · 2024
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Challenges of using pre-trained models: the practitioners’ perspective, in: Proceedings of the 31st IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER)), IEEE. pp. 67–78
Tan, X., Li, T., Chen, R., Liu, F., Zhang, L., 2024 · 2024
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Magis: Llm-based multi-agent framework for github issue resolution
Tao, W., Zhou, Y., Zhang, W., Cheng, Y., 2024 · 2024
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Deep learning model reuse in the huggingface community: Challenges, benefit and trends
Taraghi, M., Dorcelus, G., Foundjem, A., Tambon, F., Khomh, F., 2024 · 2024
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Dataset of the Paper “Understanding the Issues, Causes and Solutions in LLM Open-Source Projects”
Cai, Y., Liang, P., Wang, Y., Li, Z., Shahin, M., 2024 · 2024
Cited alongside, same era.
Analyzing the evolution and maintenance of ml models on hugging face
Castaño, J., Martínez-Fernández, S., Franch, X., Bogner, J., 2024 · 2024
Cited alongside, same era.
LangChain
Chase, H., 2024 · 2024
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Large language models for software engineering: A systematic literature review
Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J., Wang, H., 2024 · 2024
Cited alongside, same era.
An empirical study of chatgpt-related projects and their issues on github
Lin, Z., Zhang, N., Liu, C., Zheng, Z., 2024 · 2024
Cited alongside, same era.
semanric-kernel
Microsoft, 2024 · 2024
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A survey of large language models
Zhao, W.X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., Liu, P., Nie, J.Y., Wen, J.R., 2023a
Cited in the paper.
A large-scale empirical study of real-life performance issues in open source projects
Zhao, Y., Xiao, L., Bondi, A.B., Chen, B., Liu, Y., 2023b
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Tufano, R., Mastropaolo, A., Pepe, F., Dabić, O., Penta, M.D., Bavota, G., 2024 · 2024
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Large language models as software components: A taxonomy for llm-integrated applications
Weber, I., 2024 · 2024
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Towards more realistic evaluation of llm-based code generation: an experimental study and beyond
Zheng, D., Wang, Y., Shi, E., Zhang, R., Ma, Y., Zhang, H., Zheng, Z., 2024 · 2024
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Zhou, X., He, J., Ke, Y., Zhu, G., Gutiérrez-Basulto, V., Pan, J.Z., 2024 · 2024
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Security weaknesses of copilot-generated code in github projects: An empirical study
Fu, Y., Liang, P., Tahir, A., Li, Z., Shahin, M., Yu, J., Chen, J., 2025 · 2025
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