PMD VS Error Prone

PMD and Checkstyle are static analysis tools that check your code on each project build. Gradle allows to apply them easily.

The generated classes should be put into .gitignore. Otherwise, if you have Checkstyle, PMD, or SonarQube in your project, then generated classes can violate some rules. Besides, if you don't put them into .gitignore, then each pull request might become huge due to the fact that even a slightest fix can lead to lots of changes in the generated classes.

- PMD Static Code Analysis tool: https://pmd.github.io/

PMD, Spotbugs, Nullaway: Java linting/static analysis (https://pmd.github.io, https://spotbugs.github.io, https://github.com/uber/NullAway)

PMD, and checkstyle as well.

Nearly every programming language has a static analyzer of its own. For example, golang has gofmt, which is baked into the standard tooling, while Ruby has Rubocop, a community-led project. Even compiled languages like C have their own static analyzer through astyle. However, it can be difficult (and tedious) to run several analyzers across polyglot projects. Fortunately, that’s where a project like PMD can be of assistance. PMD is a static analyzer that allows you to define a standard set of rules that can be applied over multiple languages.

Open up the config/ruleset.xml file, and you’ll find an XML document that lists several rules. These rules map to the issues which PMD will report on. Believe it or not, there are hundreds of Apex rules, and you can find the full set at the PMD repo. You have complete control over which rules to enable. Typically, you’d determine which ones are important by agreeing with your teammates on the ones that matter most. After all, their code will be statically analyzed, too!

While at it you could also point them to static code analyzers such as error_prone, spotbugs and pmd (use all 3 at once - they complement each other in detecting different issues).

For PMD specifically, we use the PMD command line tool (Github) and wire it together with some bash scripting. Most pipelines will allow you to write bash as needed. The SFDX scanner command didn't exist when we implemented this, you might be able to use that instead.

This is the definition of cohesion and there are many great tools to calculate cohesion metrics (depending on the programming language e.g Java). Cohesion metrics belong to a bigger set of metrics called OOP metrics (or ck metrics). Check out the following links: https://github.com/mauricioaniche/ck https://github.com/cqfn/jpeek https://github.com/rodhilton/jasome https://github.com/pmd/pmd

A special kind of validation is enforcing that record fields are not null. (Un)fortunately, records do not have any special behavior regarding nullability. You can use tools like NullAway or Error Prone to prevent null in your code in general, or you can add checks to your records:

I am all in favour to more third side libraries adding functionalities, like Lombok, Manifold and error prone. As well as smaller projects like this shameless plug and what appears in this list

If only Google didn't suck when it came to Java9+ support... https://github.com/google/error-prone/issues/2649

I haven't used it. I use Google's ErrorProne + Lombok to prevent NPEs in java.

Take a look at NullAway, a plugin for Error Prone.

While at it you could also point them to static code analyzers such as error_prone, spotbugs and pmd (use all 3 at once - they complement each other in detecting different issues).

5. Error Prone

As Refaster has some limitations on what you can achieve (e.g., you can't perform exception type migrations), then you have to implement your own ErrorProne check operating on the AST (e.g., TryFailRefactoring to get you to assertThrows from pre-JUnit 4.13 idiom of try/fail/catch/verify).