Coverband
Scientist
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Coverband | Scientist | |
---|---|---|
6 | 18 | |
2,384 | 7,331 | |
- | 0.3% | |
8.3 | 2.5 | |
10 days ago | 30 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.
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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.
Coverband
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Dead code or unused code removal in ruby on rails.
Try https://github.com/danmayer/coverband
- How do I find all callbacks that could be executed before a controller action.
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Gem for visualizing executed code
Interesting idea! Not the same thing, but it reminded me quite a bit of Coverband
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How to Improve Code Quality on a Ruby on Rails Application
Find dead code with Coverband, which can be run in production.
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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.
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Top 8 Tools for Ruby on Rails Code Optimization and Cleanup.
I'd add Coverband. Also, since you mention rack-mini-profiler, quick plug for rails-mini-profiler, which is my own spin on performance profiling for rails apps. Still WIP though.
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.
What are some alternatives?
SimpleCov - Code coverage for Ruby with a powerful configuration library and automatic merging of coverage across test suites
Rubocop - A Ruby static code analyzer and formatter, based on the community Ruby style guide. [Moved to: https://github.com/rubocop/rubocop]
MetricFu - A fist full of code metrics
Reek - Code smell detector for Ruby
Rubycritic - A Ruby code quality reporter
bundler-leak - Known-leaky gems verification for bundler: `bundle leak` to check your app and find leaky gems in your Gemfile :gem::droplet:
Traceroute - A Rake task gem that helps you find the unused routes and controller actions for your Rails 3+ app
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.