Aaron Councilman

Aaron Councilman, David Fu, Aryan Gupta et al.

In the past few years LLMs have emerged as a tool that can aid programmers by taking natural language descriptions and generating code based on it. However, LLMs often generate incorrect code that users need to fix and the literature suggests users often struggle to detect these errors. In this work we seek to offer formal guarantees of correctness to LLM generated code; such guarantees could improve the experience of using AI Code Assistants and potentially enable natural language programming for users with little or no programming knowledge. To address this challenge we propose to incorporate a formal query language that can represent a user's intent in a formally defined but natural language-like manner that a user can confirm matches their intent. Then, using such a query we propose to verify LLM generated code to ensure it matches the user's intent. We implement these ideas in our system, Astrogator, for the Ansible programming language which includes such a formal query language, a calculus for representing the behavior of Ansible programs, and a symbolic interpreter which is used for the verification. On a benchmark suite of 21 code-generation tasks, our verifier is able to verify correct code in 83% of cases and identify incorrect code in 92%.

David Jiahao Fu, Aryan Gupta, Aaron Councilman et al.

Recent advancements in large language models (LLMs) have shown very impressive capabilities in code generation across many programming languages. However, even state-of-the-art LLMs generate programs that contains syntactic errors and fail to complete the given tasks, especially for low-resource programming languages (LRPLs). In addition, high training cost makes finetuning LLMs unaffordable with constrained computational resources, further undermining the effectiveness of LLMs for code generation. In this work, we propose SLMFix, a novel code generation pipeline that leverages a small language model (SLM) finetuned using reinforcement learning (RL) techniques to fix syntactic errors in LLM-generated programs to improve the quality of LLM-generated programs for domain-specific languages (DSLs). In specific, we applied RL on the SLM for the program repair task using a reward calculated using both a static validator and a static semantic similarity metric. Our experimental results demonstrate the effectiveness and generalizability of our approach across multiple DSLs, achieving more than 95% pass rate on the static validator. Notably, SLMFix brings substantial improvement to the base model and outperforms supervised finetuning approach even for 7B models on a LRPL, showing the potential of our approach as an alternative to traditional finetuning approaches.