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Large Language Models (LLMs) are widely adopted for assisting in software development tasks, yet their performance evaluations have narrowly focused on the functional correctness of generated code.
The treatment of non-functional requirements in mike
Dieter Landes and Rudi Studer · 1995
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The gem5 simulator
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Non-functional requirements in software engineering , volume 5
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Spandan Garg, Roshanak Zilouchian Moghaddam, Colin B Clement, Neel Sundaresan, and Chen Wu · 2022
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Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven Chu Hong Hoi · 2022
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Competition-level code generation with alphacode
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Styler: learning formatting conventions to repair checkstyle violations
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, and Ramesh Karri · 2022
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Sean Welleck, Ximing Lu, Peter West, Faeze Brahman, Tianxiao Shen, Daniel Khashabi, and Yejin Choi · 2022
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Purple llama cyberseceval: A secure coding benchmark for language models
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Open problems and fundamental limitations of reinforcement learning from human feedback
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Teaching large language models to self-debug
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Llama 2: Open foundation and fine-tuned chat models
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