Codecov聽vs聽Coverity Scan

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Codecov
Codecov

230
150
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64
Coverity Scan
Coverity Scan

8
14
+ 1
0
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Codecov vs Coverity Scan: What are the differences?

Codecov: Hosted coverage reports with awesome features to enhance your CI workflow. Our patrons rave about our elegant coverage reports, integrated pull request comments, interactive commit graphs, our Chrome plugin and security; Coverity Scan: Find and fix defects in your Java, C/C++ or C# open source project for free. Coverity's implementation of static analysis can follow all the possible paths of execution through source code (including interprocedurally) and find defects and vulnerabilities caused by the conjunction of statements that are not errors independent of each other.

Codecov belongs to "Code Coverage" category of the tech stack, while Coverity Scan can be primarily classified under "Code Review".

Some of the features offered by Codecov are:

  • Beautiful Reports
  • Pull Request Comments
  • Interactive Commit Graphs

On the other hand, Coverity Scan provides the following key features:

  • Test every line of code and potential execution path.
  • The root cause of each defect is clearly explained, making it easy to fix bugs
  • Integrates with GitHub and Travis CI
- No public GitHub repository available -
- No public GitHub repository available -

What is Codecov?

Our patrons rave about our elegant coverage reports, integrated pull request comments, interactive commit graphs, our Chrome plugin and security.

What is Coverity Scan?

Coverity's implementation of static analysis can follow all the possible paths of execution through source code (including interprocedurally) and find defects and vulnerabilities caused by the conjunction of statements that are not errors independent of each other.
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Why do developers choose Codecov?
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      What are some alternatives to Codecov and Coverity Scan?
      Code Climate
      After each Git push, Code Climate analyzes your code for complexity, duplication, and common smells to determine changes in quality and surface technical debt hotspots.
      Coveralls
      Coveralls works with your CI server and sifts through your coverage data to find issues you didn't even know you had before they become a problem. Free for open source, pro accounts for private repos, instant sign up with GitHub OAuth.
      SonarQube
      SonarQube provides an overview of the overall health of your source code and even more importantly, it highlights issues found on new code. With a Quality Gate set on your project, you will simply fix the Leak and start mechanically improving.
      Codacy
      Codacy is an automated code review tool for Scala, Java, Ruby, JavaScript, PHP, Python, CoffeeScript and CSS. It's continuous static analysis without the hassle. Save time in Code Reviews. Tackle your technical debt
      uberalls
      Code coverage metric storage service. Provide coverage metrics on differentials with Phabricator and Jenkins, just like Coveralls does for GitHub and TravisCI.
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      Decisions about Codecov and Coverity Scan
      Tim Abbott
      Tim Abbott
      Founder at Zulip | 4 upvotes 9.5K views
      atZulipZulip
      Coveralls
      Coveralls
      Codecov
      Codecov

      We use Codecov because it's a lot better than Coveralls. Both of them provide the useful feature of having nice web-accessible reports of which files have what level of test coverage (though every coverage tool produces reasonably nice HTML in a directory on the local filesystem), and can report on PRs cases where significant new code was added without test coverage.

      That said, I'm pretty unhappy with both of them for our use case. The fundamental problem with both of them is that they don't handle the ~1% probability situations with missing data due to networking flakiness well. The reason I think our use case is relevant is that we submit coverage data from multiple jobs (one that runs our frontend test suite and another that runs our backend test suite), and the coverage provider is responsible for combining that data together.

      I think the problem is if a test suite runs successfully but due to some operational/networking error between Travis/CircleCI and Codecov the coverage data for part of the codebase doesn't get submitted, Codecov will report a huge coverage drop in a way that is very confusing for our contributors (because they experience it as "why did the coverage drop 12%, all I did was added a test").

      We migrated from Coveralls to Codecov because empirically this sort of breakage happened 10x less on Codecov, but it still happens way more often than I'd like.

      I wish they put more effort in their retry mechanism and/or providing clearer debugging information (E.g. a big "Missing data" banner) so that one didn't need to be specifically told to ignore Codecov/Coveralls when it reports a giant coverage drop.

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      How developers use Codecov and Coverity Scan
      Avatar of Dieter Adriaenssens
      Dieter Adriaenssens uses Coverity ScanCoverity Scan

      runs security checks on the code

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