[R] Interactive Code Generation via Test-Driven User-Intent Formalization - TICODER - Microsoft Research 2022 - Improves the pass@1 code generation accuracy metric from 48.39% to 70.49% with a single user query, and up to 85.48% with up to 5 user que

Paper: [https://arxiv.org/abs/2208.05950](https://arxiv.org/abs/2208.05950)

Abstract:

>Pre-trained large language models (LLMs) such as OpenAI Codex have shown immense potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent. However, the code produced does not have any correctness guarantees around satisfying user's intent. In fact, it is hard to define a notion of correctness since natural language can be ambiguous and lacks a formal semantics. In this paper, we take a first step towards addressing the problem …

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