Tortoise: Interactive System Configuration Repair

TL;DR

This paper introduces Tortoise, a synthesis-based system that enables safe and maintainable repair of system configuration specifications written in languages like Puppet, by harmonizing shell fixes with high-level configuration repairs.

Contribution

It presents a novel technique that automatically repairs configuration language specifications to align with shell-based fixes, ensuring safety, maintainability, and usability.

Findings

Top-ranked repair chosen by humans 76% of the time

Average human-equivalent repair ranking is 1.31

Effective on 42 repair scenarios with benchmarks

Abstract

System configuration languages provide powerful abstractions that simplify managing large-scale, networked systems. Thousands of organizations now use configuration languages, such as Puppet. However, specifications written in configuration languages can have bugs and the shell remains the simplest way to debug a misconfigured system. Unfortunately, it is unsafe to use the shell to fix problems when a system configuration language is in use: a fix applied from the shell may cause the system to drift from the state specified by the configuration language. Thus, despite their advantages, configuration languages force system administrators to give up the simplicity and familiarity of the shell. This paper presents a synthesis-based technique that allows administrators to use configuration languages and the shell in harmony. Administrators can fix errors using the shell and the technique automatically repairs the higher-level specification written in the configuration language. The approach (1) produces repairs that are consistent with the fix made using the shell; (2) produces repairs that are maintainable by minimizing edits made to the original specification; (3) ranks and presents multiple repairs when relevant; and (4) supports all shells the administrator may wish to use. We implement our technique for Puppet, a widely used system configuration language, and evaluate it on a suite of benchmarks under 42 repair scenarios. The top-ranked repair is selected by humans 76% of the time and the human-equivalent repair is ranked 1.31 on average.