BashCoder-R1: Towards Robust and Explainable Bash Code Generation with Robustness-Aware Group Relative Policy Optimization

Abstract

Bash scripts are critical for system administration, DevOps, and CI/CD, where code quality affects stability and security. However, LLM-generated scripts often lack reasoning and contain robustness flaws such as mishandled edge cases and unchecked failures. This is particularly critical in production environments where even minor errors can lead to service disruptions. We propose BashCoder-R1, a framework that jointly addresses both issues by treating explainability as a design goal. The pipeline has three stages. Continual Pre-training adapts to Bash syntax. Long Chain-of-Thought Supervised Fine-Tuning on expert-validated samples teaches risk-averse reasoning before code generation. Robustness-Aware Group Relative Policy Optimization optimizes a weighted reward for syntax correctness, robustness (verified by shellcheck), and format adherence. This staged design ensures that the model progressively acquires syntax knowledge, reasoning capability, and robust decision-making. On our BashBench benchmark (952 real-world tasks, 773 single-line and 179 multi-line), BashCoder-R1 achieves SyntaxPass of 100.00/94.97, RobustWarnRate of 4.01/16.47, RobustPass of 95.99/79.33, FuncRate of 93.01/93.85, and FullRate of 90.04/73.18 for single-line and multi-line tasks, respectively. These are relative FullRate improvements of 37.82 and 20.18 percent over the strongest baseline, DeepSeek-V3.2 (Reasoning). Human evaluation confirms its reasoning chains are highest in quality.

Accepted to ISSTA 2026