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.