hound VS CodeClimate

The same algorithm is also used in Hound (https://github.com/hound-search/hound) though I have to say the best implementation of code search by far that I've seen is https://grep.app

You really should check it out if you haven't already. It's incredibly useful; I used it all the time. Not open source though.

There is also Hound [8].

[8]: https://github.com/hound-search/hound

Agreed, I already have Hound setup to search across all the different repos I pull from (bitbucket, gh, gitlab, gitea etc) but now I need to find a docker equivalent.

i know you're looking for first-party tools that is part of the whole package, but hound does this fantastically and is extremely easy to setup, and is ridiculously fast.

Especially if this is long term, this is a great tool:

https://github.com/hound-search/hound#hound

It would be great if someone integrated this with tree-sitter plus something to make the search semantics a bit smarter about usages of X:

https://www.etsy.com/codeascraft/announcing-hound-a-lightnin...

Screenshots:

https://jaxenter.com/hound-go-react-code-search-engine-15008...

Another trick I use for Java: javap all the Enums out of the compiled artifacts; these indicate weird things like "modes" that you can use to start asking questions relevant to the domain. Like "why are there four ways to reprice an invoice" or finding the "types" of fees or w/e in a billing system. (assuming enum classes are used)

Nice too that it's a compiled language, so you get the end tool in a nice static binary. As a non-Node dev, I hate the experience of hacking on some project and having to install a giant pool of NPM stuff just run some minifier or linter. Hound is an example of this— the guts of the project are golang, but it has a frontend that uses webpack, jest, etc: https://github.com/hound-search/hound

Which is fine, I guess; definitely use the right tool for the job. And maybe Node developers hate finding my Python projects and needing to set up a virtualenv to run them in. But all the same, I approve a direction where more of this kind of tooling is available without a build-time Node dependency.

[2] https://about.sourcegraph.com/

[3] https://oracle.github.io/opengrok/

[4] https://github.com/hound-search/hound

Resource Link: CodeClimate

Want to know how to enforce allowing only high-quality software into production? Check out this post on how to use CodeClimate can help you do just that! #DevOps #SoftwareDeveloper #softwaredevelopment #SoftwareEngineering #webdevelopment #codequality

Ideally, software can quickly go from development to production. Continuous deployment and delivery are some processes that make this possible. Continuous deployment means establishing an automated pipeline from development to production while continuous delivery means maintaining the main branch in a deployable state so that a deployment can be requested at any time. Predecos uses these tools. When a commit goes into master, the code is pushed directly to the public environment. Deployment also occurs when a push is made to a development branch enabling local/e2e testing before push to master. In this manner the master branch can be kept clean and ready for deployment most of the time. Problems that surface resulting from changes are visible before reaching master. Additional automated tools are used. Docker images are built for each microservice on commit to a development or master branch, a static code analysis is performed by SonarCloud revealing quality and security problems, Snyk provides vulnerability analysis and CodeClimate provides feedback on code quality while Coveralls provides test coverage. Finally, a CircleCI build is done. Each of these components use badges which give a heads-up display of the health of the system being developed. Incorporating each of these tools into the development process will keep the code on a trajectory of stability. For example, eliminating code smells, security vulnerabilities, and broken tests before merging a pull-request (PR) into master. Using Husky on development machines to ensure that code is well linted and locally tested before it is allowed to be pushed to source-control management (SCM). Applying additional processes such as writing tests around bugs meaning reintroduction of a given bug would cause a test to fail. The automated tools would then require that test to be fixed before push to SCM meaning fewer bugs will be reintroduced. Proper development processes and automation have a strong synergy.

The new code should not drop existing code coverage I've found in practice mainly catches changes to existing code that lack proper updates to existing tests. Our company uses Code Climate for these checks, so we don't have to manage / write our own tooling for this purpose.

Code Climate

Some exciting NYC companies tech companies I like are alloy.co, hyperscience.com, vanta.com, and codeclimate.com.

Best code quality - Thanks to SaaS such as CodeClimate I can tell when my code quality is too low. I try to keep "code smells" absolute zero.

Maintainable with smaller methods, less complexity – To know more on this make a habit of using code analyzer like rubocop, Code Climate