It Will Never Work in Theory

Too Many Knobs

Posted Jun 9, 2016 by Greg Wilson

Tianyin Xu, Long Jin, Xuepeng Fan, Yuanyuan Zhou, Shankar Pasupathy, and Rukma Talwadker: "Hey, You Have Given Me Too Many Knobs! Understanding and Dealing with Over-Designed Configuration in System Software". ESEC/FSE'15, August 2015, http://dx.doi.org/10.1145/2786805.2786852, http://cseweb.ucsd.edu/~tixu/papers/fse15.pdf.

This paper makes a first step in understanding a fundamental question of configuration design: "do users really need so many knobs?" To provide the quantitatively answer, we study the configuration settings of real-world users, including thousands of customers of a commercial storage system (Storage-A), and hundreds of users of two widely-used open-source system software projects. Our study reveals a series of interesting findings to motivate software architects and developers to be more cautious and disciplined in configuration design. Motivated by these findings, we providea few concrete, practical guidelines which can significantly reduce the configuration space. Take Storage-A as an example, the guidelines can remove 51.9% of its parameters and simplify 19.7% of the remaining ones with little impact on existing users. Also, we study the existing configuration navigation methods in the context of "too many knobs" to understand their effectiveness in dealing with the over-designed configuration, and to provide practices for building navigation support in system software.

I can't write a better summary of the paper than the authors have themselves:

Only a small percentage (6.1%-16.7%) of configuration parameters are set by the majority of users; a significant percentage (up to 54.1%) of parameters are rarely set by any user.

A small percentage (1.8%-7.8%) of parameters are configured by more than 90% of the users.

Software developers often choose more "flexible" data types for configuration parameters to give users more flexibility of settings (e.g., using numeric types instead of the simple Boolean or enumerative ones). However, users seem not to take full advantage of such flexibility. A significant percentage (up to 47.4%) of numeric parameters have no more than five distinct settings among all the users' settings.

Similarly, for enumerative parameters with many options, typically only two to three of the options are actually used by the users, indicating once again the over-designed flexibility.

Too many knobs do come with a cost: users encounter tremendous difficulties in knowing which parameters should be set among the large configuration space. This is reflected by the following two facts: (1) a significant percentage (up to 48.5%) of configuration issues are about users' difficulties in finding or setting the parameters to obtain the intended system behavior; (2) a significant percentage (up to 53.3%) of configuration errors are introduced due to users' staying with default values incorrectly.

Configuration parameters with explicit semantics, visible external impact are set by more users, in comparison to parameters that are specific to internal system implementation.

The configuration of the studied software can be significantly simplified by reducing the configuration space both vertically and horizontally. For Storage-A, 51.9% of the original parameters can be hidden or removed, and 19.7% of the remaining ones can be further converted into simpler types, with the impact on fewer than 1% of the users. The similar reduction rates are also observed in the other two open-source software.

Searching user manuals by keywords is not efficient to help users identify the target parameter(s).

Google search can provide useful information for 46.1%-80.0% of the historical configuration navigation issues. However, it is less efficient in navigation parameters of less popular software or new issues. The majority of resources on the Web that host useful information for navigation are the contents contributed by users, such as Q&A forums and blog articles.

Well-engineered NLP-based navigation can return the target configuration parameter for more than 60% of the historical navigation issues. Boosting the results with the statistics of users' configuration settings in the field can significantly improve the performance of NLP-based navigation.

There's lots more in here: discussion of the experimental method used, a table of recommendations for simplifying configuration whose points are all grounded in findings, and pointers to related work (much of which I hadn't seen before). What's more, the configuration data is available in a GitHub repository for those who wish to examine it themselves.

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