PMD VS infer

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

Meta/Facebook are long time OCaml users, their logo is on the OCaml website. Their static analysis tool and its predecessor are both written in OCaml.

Efforts: Dependabot, CodeQL, Coverity, facebook's Infer tool, etc

I notice there isn't fbinfer. It's pretty cool, and is used for this library.

TBH, there's a non-zero amount of non-"ivory tower" tools you may have used that are written in functional languages. Say, Pandoc or Shellcheck are written in Haskell; Infer and Flow are written in OCaml. RabbitMQ and Whatsapp are implemented in Erlang (FB Messenger was too, originally; they switched to the C++ servers later). Twitter backend is (or was, at least) written in Scala.

- borrow-cpp which exploits some null dereference checks in the infer static analyzer to model some of borrow checking.

> Maybe you could write tests as queries that would test a whole set of possible programs, not only the current version of your program at the moment.

I think that the future of programming is more sophisticated static analysis. Programmers will write statements like, "every code path that writes to the Payments database must have called validate_user()." Then, the tooling will confirm that rule with every commit.

We kind of have this already (for example, Facebook's Infer tool [0]), but I think it will become much more important in the coming decade.

0: https://github.com/facebook/infer

Great question! Formal methods groups in industry are growing rapidly and popping up in surprising places. Amazon's group is probably the most famous, but I think pretty much every big tech company has something going on in the formal verification / static analysis space. There's also a lot going on in blockchain . It's definitely becoming harder to hire people with FM skills, so in that sense, I think it's a great space to get into.

The downside is that the space is quite fragmented and a lot of tools have a high skill bar. If I was starting out, I'd probably focus on static analysis (eg. Infer or something similar - https://github.com/facebook/infer) because those tools tend to be easier to learn, and they have the potential to scale to really big systems. In contrast, Coq is a fine tool, but most people learn it by going to grad school which isn't useful short term career advice.

There are lot of great interviews with practitioners on the Galois podcast, Building Better Systems - that might be a good place to start exploring: https://www.stitcher.com/show/building-better-systems

> The only reliable way to have bug-free code is to prove it. It requires solid mathematical foundations and a programming language that allows formal proofs.

I'm going to be the "actually" guy and say that, actually, you can formally verify some studff about programs written in traditional/mainstream languages, like C. Matter of fact, this is a pretty lively research area, with some tools like CBMC [0] and Infer [1] also getting significant adoption in the industry.

[0]: https://github.com/diffblue/cbmc

[1]: https://fbinfer.com/