molecule VS CrossHair

Hell, the ansible roles I maintain use Molecule for testing.

The Ansible team announced at AnsibleFest and Ansible Community Days last month that Molecule will be released as a developer preview on its way to being a fully supported part of the Ansible platform. In order to achieve this, Molecule will need to be streamlined and refocused as a test runner for functional testing of Ansible playbooks and roles using Ansible itself. This will require a number of breaking changes to happen before releasing “Molecule 6” in order to do all of this. You can read about the changes here and we welcome your questions and feedback on their roll out: https://github.com/ansible-community/molecule/milestone/35

I would recommend you to use roles instead of just playbooks and to test them with molecule. Molecule allows you to quickly test your Ansible roles in a fresh Arch Linux podman container, completely isolated from your real system.

Please post comments on https://github.com/ansible-community/molecule/discussions/3555

Projects to make it easier to write and test Ansible Content. Includes VScode extension, language server, ansible-lint, molecule, ansible-navigator and potentially other development goodies. To see what's planned, and how you can help checkout the foundation-devtools project board

I’m assuming you are using Molecule: https://github.com/ansible-community/molecule

Please upvote your prefered answer from the https://github.com/ansible-community/molecule/discussions/3130 survey which proposed joining ansible-lint and ansible-molecule channels when we move them from freenode irc to libera.chat. There is also an option to keep the channels separated.

Just as a heads up, Molecule has a very active discussion area on GitHub.

Ansible Molecule

But to be fair, a lot has changed since 2017 :)

Writing some Python for Hacktoberfest? Try out CrossHair while you do that and get credit for a blog post too! https://github.com/pschanely/CrossHair/issues/173

CrossHair, Hypothesis, and Mutmut for advanced testing.

When you say "formal verification methods", what kind of techniques are you interested in? While using interactive theorem provers will most likely not become very widespread, there are plenty of tools that use formal techniques to give more correctness guarantees. These tools might give some guarantees, but do not guarantee complete functional correctness. WireGuard (VPN tunnel) is I think a very interesting application where they verified the protocol. There are also some tools in use, e.g. Mythril and CrossHair, that focus on detecting bugs using symbolic execution. There's also INFER from Facebook/Meta which tries to verify memory safety automatically. The following GitHub repo might also interest you, it lists some companies that use formal methods: practical-fm

The main difference that Klara bring to the table, compared to similar tool like pynguin and Crosshair is that the analysis is entirely static, meaning that no user code will be executed, and you can easily extend the test generation strategy via plugin loading (e.g. the options arg to the Component object returned from function above is not needed for test coverage).

Just in case you are looking for an alternative approach: if you write contracts in your code, you might also consider crosshair [1] or icontract-hypothesis [2]. If your function/method does not need any pre-conditions then the the type annotations can be directly used.

(I'm one of the authors of icontract-hypothesis.)

[1] https://github.com/pschanely/CrossHair

[2] https://github.com/mristin/icontract-hypothesis

There's a tool for verification of Python programs based on contracts which uses Z3: https://github.com/pschanely/CrossHair

You can use it as part of your CI or during the development (there's even a neat "watch" mode, akin to auto-correct).

Looking at some of your SMT-based projects, I'd love to compare your SMT solver notes with my mine from working on https://github.com/pschanely/CrossHair

Sadly, there aren't a lot of resources on how to use SMT solvers well.