Scientist
SimpleCov
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Scientist | SimpleCov | |
---|---|---|
18 | 11 | |
7,331 | 4,707 | |
0.3% | 0.2% | |
2.5 | 6.5 | |
about 1 month ago | 9 days ago | |
Ruby | Ruby | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Scientist
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Crates that run multiple versions of a function and ensures the return value is the same?
For some google-fu, the ruby / .NET equivalent of this is https://github.com/github/scientist / https://github.com/scientistproject/Scientist.net
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Scientist: A Ruby library for carefully refactoring critical paths
The readme (here https://github.com/github/scientist#alternatives) doesn't mention, but here is one for Rust: https://crates.io/crates/scientisto
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Test Against Reality
Something I've learned in Ruby land (prob standard in other places, forgive my ignorance) that seems a bit different than what the article advocates for (fake services):
- Write your service wrapper (eg your logic to interact with Twilio)
- Call the service and record API outputs, save those as fixtures that will be returned as responses in your tests without hitting the real thing (eg VCR, WebMock)
- You can now run your tests against old responses (this runs your logic except for getting a real response from the 3rd party; this approach leaves you exposed to API changes or you have edge cases not handled)
For the last part, two approaches to overcome this:
- Wrap any new logic in try/catch and report to Sentry: you avoid breaking prod and get info on new edge cases you didn't cover (this may not be feasible if the path where you're inserting new logic into does not work at all without the new feature; address this with thoughtful design/rollout of new features)
- Run new logic side by side to see what happens to the new logic when running in production (https://github.com/github/scientist)
I use the first approach bc small startup.
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Real-World Engineering Challenges: Migrations
Check out GitHub scientist if you are doing a migration with a ruby based system: https://github.com/github/scientist
Great support and functionality for testing differences between two systems of record.
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Rethinking Testing
As far as this idea, I have seen this before in a few different forms. The closest thing that I've personally witnessed being used is the scientist gem for Ruby applications. You have to do it manually, but you can instrument your code to compare old and new versions of some code. It also does some fancy stuff like randomly choosing which version gets run, almost like an A/B test. I wonder if there's a similar library for Python?
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axum-strangler initial release
Not sure what OP had in mind, but for my dream strangler (that's a phrase I never expected to use), I'd love functionality like github's scientist library; basically, the ability to implement a route, continue to serve most requests through the original service, but duplicate a small percentage to the new implementation, compare the outputs of the two services, and log wherever the responses differ, so you get live production tests to exercise the new service without impacting users.
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Using Scientist to Refactor Critical Ruby on Rails Code
However, the good news is that it’s easy and safe to do so in Ruby and Rails using the Scientist gem. Scientist's name is based on the scientific method of conducting experiments to verify a given hypothesis. In this case, our hypothesis is that the new code does the job.
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Book notes: Turn the Ship Around!
Github scientist.
SimpleCov
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Must-have gems for mature Rails
gem "simplecov" - https://github.com/simplecov-ruby/simplecov | Gather spec coverage stats locally and on CI, aim for those 90+%.
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Evaluating More Coverage in Ruby 3.2
Have you wondered how much of the logic in your views is exercised in your test suite? Thanks to this change, now you can see that in tools like SimpleCov.
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My First Code Commit in Ruby
My talk is about different best practices - specifically when adhering to them breaks down. One of those best practices is high test coverage. I start to work on the content for my presentation by building the code samples that I want to use in the slides. For the code coverage section, I'm writing some code with some tests. I'm using SimpleCov to generate code coverage results.
- Falha de cobertura: Divagações sobre testes de software
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Improve Code in Your Ruby Application with RubyCritic
SimpleCov - a tool to check Ruby application code coverage. You can configure it to run alongside your tests. It provides metrics on code coverage so that you can identify what you need to pay attention to and where to invest your time to create better test cases.
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Paying Down Technical Debt
Ensure that you have sufficient test coverage. You can use code coverage analysis tools like SimpleCov to gain insight into gaps in your coverage.
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How to test all workers in one big loop?
simplecov might the answer you need, it generates a report of the lines of code your test suite hits.
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How to Improve Code Quality on a Ruby on Rails Application
Use SimpleCov to generate a report of how many statements are covered by your test suite. It won't assess the test suite quality, though.
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Ruby's Got You Covered
There are many tools for measuring test coverage, but one is SimpleCov. It also supports branches coverage. To measure coverage of production code, check out Coverband, which you can set up to use oneshot lines mode.
- Como configurar ambiente de testes em Ruby on Rails com RSpec
What are some alternatives?
Rubocop - A Ruby static code analyzer and formatter, based on the community Ruby style guide. [Moved to: https://github.com/rubocop/rubocop]
Coverband - Ruby production code coverage collection and reporting (line of code usage)
Rubycritic - A Ruby code quality reporter
Traceroute - A Rake task gem that helps you find the unused routes and controller actions for your Rails 3+ app
undercover - undercover warns about methods, classes and blocks that were changed without tests, to help you easily find untested code and reduce the number of bugs. It does so by analysing data from git diffs, code structure and SimpleCov coverage reports
Flog - Flog reports the most tortured code in an easy to read pain report. The higher the score, the more pain the code is in.
Pronto - Quick automated code review of your changes
bundler-leak - Known-leaky gems verification for bundler: `bundle leak` to check your app and find leaky gems in your Gemfile :gem::droplet:
rails_best_practices - a code metric tool for rails projects