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- faba
Fabrictestbed-extensions documentation — fabric-fablib.readthedocs.io. https://fabric-fablib.readthedocs.io/en/stable/. [Accessed 10-04-2025].
- mis
Codestral | Mistral AI — mistral.ai. https://mistral.ai/news/codestral. [Accessed 10-04-2025].
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- fabc
FABRIC Portal — portal.fabric-testbed.net. https://portal.fabric-testbed.net/. [Accessed 10-04-2025].
- git
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- AAB+24
Marah Abdin, Jyoti Aneja, Harkirat Behl, Sébastien Bubeck, Ronen Eldan, Suriya Gunasekar, Michael Harrison, Russell J. Hewett, Mojan Javaheripi, Piero Kauffmann, James R. Lee, Yin Tat Lee, Yuanzhi Li, Weishung Liu, Caio C. T. Mendes, Anh Nguyen, Eric Price, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Xin Wang, Rachel Ward, Yue Wu, Dingli Yu, Cyril Zhang, and Yi Zhang. Phi-4 technical report. 2024. URL: https://arxiv.org/abs/2412.08905, arXiv:2412.08905.
- BM24
Nastaran Bassamzadeh and Chhaya Methani. A comparative study of dsl code generation: fine-tuning vs. optimized retrieval augmentation. arXiv preprint arXiv:2407.02742, 2024.
- EAB+24
Jessica LĂłpez Espejel, Mahaman Sanoussi Yahaya Alassan, Merieme Bouhandi, Walid Dahhane, and El Hassane Ettifouri. Low-cost language models: survey and performance evaluation on python code generation. arXiv preprint arXiv:2404.11160, 2024.
- GZY+24
Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y. Wu, Y. K. Li, Fuli Luo, Yingfei Xiong, and Wenfeng Liang. Deepseek-coder: when the large language model meets programming – the rise of code intelligence. 2024. URL: https://arxiv.org/abs/2401.14196, arXiv:2401.14196.
- HRvSikonja24
Marko Hostnik and Marko Robnik-Šikonja. Retrieval-augmented code completion for local projects using large language models. arXiv preprint arXiv:2408.05026, 2024.
- JSM+23
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. Mistral 7b. 2023. URL: https://arxiv.org/abs/2310.06825, arXiv:2310.06825.
- LPP+20
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, and others. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, 33:9459–9474, 2020.
- LWL+25
Xinze Li, Hanbin Wang, Zhenghao Liu, Shi Yu, Shuo Wang, Yukun Yan, Yukai Fu, Yu Gu, and Ge Yu. Building a coding assistant via the retrieval-augmented language model. ACM Transactions on Information Systems, 43(2):1–25, 2025.
- OJA24
OpenAI and Steven Adler et al Josh Achiam. Gpt-4 technical report. 2024. URL: https://arxiv.org/abs/2303.08774, arXiv:2303.08774.
- RGG+23
Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Romain Sauvestre, Tal Remez, and others. Code llama: open foundation models for code. arXiv preprint arXiv:2308.12950, 2023.
- SSS+24
Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, and Aman Chadha. A systematic survey of prompt engineering in large language models: techniques and applications. arXiv preprint arXiv:2402.07927, 2024.
- SAHW24
Jiho Shin, Reem Aleithan, Hadi Hemmati, and Song Wang. Retrieval-augmented test generation: how far are we? arXiv preprint arXiv:2409.12682, 2024.
- SJL+24
Hongjin Su, Shuyang Jiang, Yuhang Lai, Haoyuan Wu, Boao Shi, Che Liu, Qian Liu, and Tao Yu. Arks: active retrieval in knowledge soup for code generation. arXiv preprint arXiv:2402.12317, 2024.
- TZH+24
CodeGemma Team, Heri Zhao, Jeffrey Hui, Joshua Howland, Nam Nguyen, Siqi Zuo, Andrea Hu, Christopher A. Choquette-Choo, Jingyue Shen, Joe Kelley, Kshitij Bansal, Luke Vilnis, Mateo Wirth, Paul Michel, Peter Choy, Pratik Joshi, Ravin Kumar, Sarmad Hashmi, Shubham Agrawal, Zhitao Gong, Jane Fine, Tris Warkentin, Ale Jakse Hartman, Bin Ni, Kathy Korevec, Kelly Schaefer, and Scott Huffman. Codegemma: open code models based on gemma. 2024. URL: https://arxiv.org/abs/2406.11409, arXiv:2406.11409.
- WAY+25
Zora Zhiruo Wang, Akari Asai, Xinyan Velocity Yu, Frank F. Xu, Yiqing Xie, Graham Neubig, and Daniel Fried. Coderag-bench: can retrieval augment code generation? 2025. URL: https://arxiv.org/abs/2406.14497, arXiv:2406.14497.
- YMK+24
Sangyeop Yeo, Yu-Seung Ma, Sang Cheol Kim, Hyungkook Jun, and Taeho Kim. Framework for evaluating code generation ability of large language models. Etri Journal, 46(1):106–117, 2024.