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Bauman, D. (2018, February 16). After 2016 election, campus hate crimes seemed to jump. Here’s what the data tell us. Chronicle of Higher Education . Retrieved from https://www.chronicle.com/article/After-2016-Election-Campus/242577

Bawab, N. (2017, August 24). Appalachian State students greeted by white supremacy banner. USA Today College . Retrieved from Celine Vintage Macadam Pattern Tote Brown wRWWlf

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Casey, J. (1989). At Dartmouth the clash of ’89. New York Times Magazine . Retrieved from Peter Non Sandals Lead o2h8jFi0s

Chemerinsky, E., Gillman, H. (2017). Free speech on campus. New Haven: Yale University Press.

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Package io and package time had few enough dependencies to be used by package os, and the Go 1 definition of os.FileInfo does use time.Time .

(As a side note, our first idea was to move os.Error and os.NewError to a new package named error (singular) as error.Value and error.New . Feedback from Roger Peppe and others in the Go community helped us see that making the error type predefined in the language would allow its use even in low-level contexts like the specification of run-time panics . Since the type was named error , the package became errors (plural) and the constructor errors.New . Andrew Gerrand’s 2015 talk “ How Go was Made ” has more detail.)

The benefits of a codebase refactoring apply throughout the codebase. Unfortunately, so do the costs: often a large number of repairs must be made as a result of the refactoring. As codebases grow, it becomes infeasible to do all the repairs at one time. The repairs must be done gradually, and the programming language must make that possible.

As a simple example, when we moved io.ByteBuffer to bytes.Buffer in 2009, the Marsèll Zipped Ankle Boots Leather Rubber Brown kcMsOKmu8w
moved two files, adjusted three makefiles, and repaired 43 other Go source files. The repairs outweighed the actual API change by a factor of twenty, and the entire codebase was only 250 files. As codebases grow, so does the repair multiplier. Similar changes in large Go codebases, such as Docker, and Juju, and Kubernetes, can have repair multipliers ranging from 10X to 100X. Inside Google we’ve seen repair multipliers well over 1000X.

The conventional wisdom is that when making a codebase-wide API change, the API change and the associated code repairs should be committed together in one big commit:

The argument in favor of this approach, which we will call “atomic code repair,” is that it is conceptually simple: by updating the API and the code repairs in the same commit, the codebase transitions in one step from the old API to the new API, without ever breaking the codebase. The atomic step avoids the need to plan for a transition during which both old and new API must coexist. In large codebases, however, the conceptual simplicity is quickly outweighed by a practical complexity: the one big commit can be very big. Big commits are hard to prepare, hard to review, and are fundamentally racing against other work in the tree. It’s easy to start doing a conversion, prepare your one big commit, finally get it submitted, and only then find out that another developer added a use of the old API while you were working. There were no merge conflicts, so you missed that use, and despite all your effort the one big commit broke the codebase. As codebases get larger, atomic code repairs become more difficult and more likely to break the codebase inadvertently.

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