Interactive Code Generation via Test-Driven User-Intent Formalization

• URL: http://arxiv.org/abs/2208.05950v2
• Date: Wed, 4 Oct 2023 01:53:15 GMT
• Title: Interactive Code Generation via Test-Driven User-Intent Formalization
• Authors: Shuvendu K. Lahiri and Sarah Fakhoury and Aaditya Naik and Georgios Sakkas and Saikat Chakraborty and Madanlal Musuvathi and Piali Choudhury and Curtis von Veh and Jeevana Priya Inala and Chenglong Wang and Jianfeng Gao
• Abstract summary: Large language models (LLMs) produce code from informal natural language (NL) intent. It is hard to define a notion of correctness since natural language can be ambiguous and lacks a formal semantics. We describe a language-agnostic abstract algorithm and a concrete implementation TiCoder.
• Score: 60.90035204567797
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have shown great potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent. However, when interacting with LLMs, users have no guarantees that the code suggestions produced correctly satisfy the intent they provided. 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 propose the workflow of {\it interactive test-driven code generation}, which leverages lightweight user feedback to (a) formalize the user intent using generated tests that can be useful for debugging, and (b) produce an improved set of code suggestions by pruning and ranking candidate code suggestions. We describe a language-agnostic abstract algorithm and a concrete implementation TiCoder. We perform an automated evaluation of TiCoder on the \emph{MBPP} and \emph{HumanEval} code generation benchmarks. Our results are promising with using the OpenAI Codex LLM: our best algorithm improves the \passk{1} code generation accuracy (in absolute percentages) between $22.49\%$ to $37.71\%$ for MBPP and between $24.79\%$ to $53.98\%$ for HumanEval using between 1 to 5 simulated user queries.

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