- author = "Aaron Weiss and Arjun Guha and Yuriy Brun",
- title = "Tortoise: Interactive System Configuration Repair",
- booktitle = "Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering, ASE 2017",
- year = "2017",
- pages = "625--636",
- address = "Urbana, IL, USA",
- month = "30 " # oct # " - 3 " # nov,
- keywords = "genetic algorithms, genetic programming, genetic improvement, APR, Puppet",
- isbn13 = "978-1-5386-3976-4",
-
URL = "
https://people.cs.umass.edu/~brun/pubs/pubs/Weiss17ase.pdf",
-
URL = "
https://doi.org/10.1109/ASE.2017.8115673",
-
DOI = "
doi:10.1109/ASE.2017.8115673",
-
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.
we present 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 76percent of the time and the human-equivalent repair is ranked 1.31 on average.",
- notes = "also known as \cite{8115673}",
