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CSI: Gopher

CSI: Gopher

If we could intelligently parse all of the open-source Go code on GitHub, what could we learn? We’re going to show you some of the interesting things we’ve found in Go projects, from library usage, idioms & package layouts, to how Gophers can use this data to make decisions about their own APIs.

Francesc Campoy Flores

April 12, 2019

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  1. • Wrote a LOT of regex • Found some neat

    bugs! • Francesc had been working with tools that made what I was doing more accessible.
  2. How do you find bugs or API (mis) usage? •

    Across microservice code-bases • In all consumers of your library? • Accurately?
  3. What’s a string? It depends: 'single quotes' "double quotes" '''triple

    single quotes''' """triple double quotes""" `back quotes` «Please» „make it“ qq§ STOP! §
  4. Universal Abstract Syntax Trees package main import "fmt" func main()

    { fmt.Println("Hello, gophers") } File Declaration Import “fmt” Declaration FunctionGroup main Body go:CallExpr fmt Println “Hello, gophers” Fun Args
  5. Universal Abstract Syntax Trees uast:String XPath: //uast:String File Declaration Import

    “fmt” Declaration FunctionGroup main Body go:CallExpr fmt Println “Hello, gophers” Fun Args “fmt” “Hello, gophers”
  6. So, how are ASTs relevant here? - We downloaded ~19GB

    of Go repositories from GitHub* - Parsed them as UAST with src-d/engine - Queried the generated databases with SQL + UAST extensions https://github.com/src-d/engine
  7. OK, so: what are some of the things we can

    investigate? - Finding ‘bad’ code - Best practices and idioms - Usage analysis of APIs
  8. "Bad crypto" - One of my favorite topics - How

    is a non-expert supposed to know that math/rand is bad vs. crypto/rand? - What about hash functions vs. KDFs?
  9. Applied the scientific method to best practices Make an observation.

    Ask a question. Form a hypothesis, or testable explanation. Make a prediction based on the hypothesis. Test the prediction. Iterate: use the results to make new hypotheses or predictions.
  10. Premise: You maintain a popular OSS library. Problem: You're thinking

    about deprecating a method, but want to quantify the impact. How do you accurately find these cases?
  11. Actual problem: • gorilla/context predates net/http's Request.Context() implementation. • Using

    both causes a memory leak, due to "islanding" the pointer to the original *Request. • Can we find these users & help?!
  12. • Order your godoc by most-used identifiers - rather than

    a bunch of ErrSomething at the top • Identify dependency version usage across your org, or as a maintainer. • Smart auto-completion based on previous usages of the API.
  13. Tools: • Google BigQuery • source{d} engine: github.com/src-d/engine • A

    lot of RAM, CPUs, and time Resources: • Finding Bugs with BigQuery & GitHub: bit.ly/gosf-bq • Analyzing Go code with BigQuery: bit.ly/go-bq
  14. #1 with 1,260,374 occurrences: '' #2 with 456,480 occurrences: 'fmt'

    #3 with 337,064 occurrences: 'json:"-"' Most common strings in Go
  15. Other languages most common strings in Python: 'automanaged' : 25,446

    '//visibility:public' : 21,962 'go_library' : 16,485 most common strings in Ruby: '\n' : 658 '' : 551 'shell' : 363 most common strings in Java: '' : 613 '\n' : 392 '0' : 293 most common strings in PHP: 'strict_param' : 1 'strict' : 1 'short_array_syntax' : 1