ShIOEnv: A Command Evaluation Environment for Grammar-Constrained Synthesis and Execution Behavior Modeling

TL;DR

This paper introduces ShIOEnv, a new Bash environment for command synthesis and execution modeling, addressing data gaps with a large dataset and a novel irreducibility signal, improving accuracy over previous methods.

Contribution

ShIOEnv provides a shell environment for command synthesis and behavior modeling, with a grammar-based argument abstraction and a self-supervised irreducibility measure, creating a large dataset for training.

Findings

Models trained on grammar-constrained data outperform prior baselines.

Higher irreducibility correlates with better execution behavior modeling.

2.1 million input-output pairs released for research use.

Abstract

Modeling of command-line interface (CLI) interaction has enabled flexible, execution-free output presentation. However, current approaches struggle to model inputs with complex compositions and inputs whose execution behavior depends on system characteristics. This is due to a lack of shell input-output (ShIO) data in the training distributions used by the models in these approaches. To address this data gap, we present ShIOEnv, a Gymnasium-compatible Bash shell environment for command synthesis and system-grounded execution behavior capturing. To concentrate synthesis on productive regions of the state-action space, we temporally abstract argument construction into grammar-derived options, thereby constraining synthesis to syntactically valid arguments. We introduce a self-supervised irreducibility signal to approximate the proportion of arguments that contribute to the observed execution behavior, serving as a measure of information density for each input. Using ShIOEnv, we curate and release 2.1M input-output pairs for modeling feedback from Bash command execution. We find that models trained on grammar-constrained datasets with higher maximum irreducibility achieve greater accuracy when modeling the execution behavior of user-sourced inputs than prior execution-free baselines.