CodeSift: An LLM-Based Reference-Less Framework for Automatic Code Validation

The advent of large language models (LLMs) has greatly facilitated code generation, but ensuring the functional correctness of generated code remains a challenge. Traditional validation methods are often time-consuming, error-prone, and impractical for large volumes of code. We introduce CodeSift, a novel framework that leverages LLMs as the first-line filter of code validation without the need for execution, reference code, or human feedback, thereby reducing the validation effort. We assess the effectiveness of our method across three diverse datasets encompassing two programming languages. Our results indicate that CodeSift outperforms state-of-the-art code evaluation methods. Internal testing conducted with subject matter experts reveals that the output generated by CodeSift is in line with human preference, reinforcing its effectiveness as a dependable automated code validation tool.

Tackling Execution-Based Evaluation for NL2Bash

Given recent advancement of Large Language Models (LLMs), the task of translating from natural language prompts to different programming languages (code generation) attracts immense attention for wide application in different domains. Specially code generation for Bash (NL2Bash) is widely used to generate Bash scripts for automating different tasks, such as performance monitoring, compilation, system administration, system diagnostics, etc. Besides code generation, validating synthetic code is critical before using them for any application. Different methods for code validation are proposed, both direct (execution evaluation) and indirect validations (i.e. exact/partial match, BLEU score). Among these, Execution-based Evaluation (EE) can validate the predicted code by comparing the execution output of model prediction and expected output in system. However, designing and implementing such an execution-based evaluation system for NL2Bash is not a trivial task. In this paper, we present a machinery for execution-based evaluation for NL2Bash. We create a set of 50 prompts to evaluate some popular LLMs for NL2Bash. We also analyze several advantages and challenges of EE such as syntactically different yet semantically equivalent Bash scripts generated by different LLMs, or syntactically correct but semantically incorrect Bash scripts, and how we capture and process them correctly.