[analyzer][NFC] Move the data structures from CheckerRegistry to the Core library

If you were around the analyzer for a while now, you must've seen a lot of
patches that awkwardly puts code from one library to the other:

* D75360 moves the constructors of CheckerManager, which lies in the Core
  library, to the Frontend library. Most the patch itself was a struggle along
  the library lines.
* D78126 had to be reverted because dependency information would be utilized
  in the Core library, but the actual data lied in the frontend.
  D78126#inline-751477 touches on this issue as well.

This stems from the often mentioned problem: the Frontend library depends on
Core and Checkers, Checkers depends on Core. The checker registry functions
(`registerMallocChecker`, etc) lie in the Checkers library in order to keep each
checker its own module. What this implies is that checker registration cannot
take place in the Core, but the Core might still want to use the data that
results from it (which checker/package is enabled, dependencies, etc).

D54436 was the patch that initiated this. Back in the days when CheckerRegistry
was super dumb and buggy, it implemented a non-documented solution to this
problem by keeping the data in the Core, and leaving the logic in the Frontend.
At the time when the patch landed, the merger to the Frontend made sense,
because the data hadn't been utilized anywhere, and the whole workaround without
any documentation made little sense to me.

So, lets put the data back where it belongs, in the Core library. This patch
introduces `CheckerRegistryData`, and turns `CheckerRegistry` into a short lived
wrapper around this data that implements the logic of checker registration. The
data is tied to CheckerManager because it is required to parse it.

Side note: I can't help but cringe at the fact how ridiculously awkward the
library lines are. I feel like I'm thinking too much inside the box, but I guess
this is just the price of keeping the checkers so modularized.

Differential Revision: https://reviews.llvm.org/D82585
10 files changed
tree: b1de591e1e48eed82b4e47b652599506d001a579
  1. clang/
  2. clang-tools-extra/
  3. compiler-rt/
  4. debuginfo-tests/
  5. flang/
  6. libc/
  7. libclc/
  8. libcxx/
  9. libcxxabi/
  10. libunwind/
  11. lld/
  12. lldb/
  13. llvm/
  14. mlir/
  15. openmp/
  16. parallel-libs/
  17. polly/
  18. pstl/
  19. utils/
  20. .arcconfig
  21. .arclint
  22. .clang-format
  23. .clang-tidy
  24. .git-blame-ignore-revs
  25. .gitignore
  26. CONTRIBUTING.md
  27. README.md
README.md

The LLVM Compiler Infrastructure

This directory and its sub-directories contain source code for LLVM, a toolkit for the construction of highly optimized compilers, optimizers, and run-time environments.

The README briefly describes how to get started with building LLVM. For more information on how to contribute to the LLVM project, please take a look at the Contributing to LLVM guide.

Getting Started with the LLVM System

Taken from https://llvm.org/docs/GettingStarted.html.

Overview

Welcome to the LLVM project!

The LLVM project has multiple components. The core of the project is itself called “LLVM”. This contains all of the tools, libraries, and header files needed to process intermediate representations and converts it into object files. Tools include an assembler, disassembler, bitcode analyzer, and bitcode optimizer. It also contains basic regression tests.

C-like languages use the Clang front end. This component compiles C, C++, Objective-C, and Objective-C++ code into LLVM bitcode -- and from there into object files, using LLVM.

Other components include: the libc++ C++ standard library, the LLD linker, and more.

Getting the Source Code and Building LLVM

The LLVM Getting Started documentation may be out of date. The Clang Getting Started page might have more accurate information.

This is an example work-flow and configuration to get and build the LLVM source:

  1. Checkout LLVM (including related sub-projects like Clang):

    • git clone https://github.com/llvm/llvm-project.git

    • Or, on windows, git clone --config core.autocrlf=false https://github.com/llvm/llvm-project.git

  2. Configure and build LLVM and Clang:

    • cd llvm-project

    • mkdir build

    • cd build

    • cmake -G <generator> [options] ../llvm

      Some common build system generators are:

      • Ninja --- for generating Ninja build files. Most llvm developers use Ninja.
      • Unix Makefiles --- for generating make-compatible parallel makefiles.
      • Visual Studio --- for generating Visual Studio projects and solutions.
      • Xcode --- for generating Xcode projects.

      Some Common options:

      • -DLLVM_ENABLE_PROJECTS='...' --- semicolon-separated list of the LLVM sub-projects you'd like to additionally build. Can include any of: clang, clang-tools-extra, libcxx, libcxxabi, libunwind, lldb, compiler-rt, lld, polly, or debuginfo-tests.

        For example, to build LLVM, Clang, libcxx, and libcxxabi, use -DLLVM_ENABLE_PROJECTS="clang;libcxx;libcxxabi".

      • -DCMAKE_INSTALL_PREFIX=directory --- Specify for directory the full path name of where you want the LLVM tools and libraries to be installed (default /usr/local).

      • -DCMAKE_BUILD_TYPE=type --- Valid options for type are Debug, Release, RelWithDebInfo, and MinSizeRel. Default is Debug.

      • -DLLVM_ENABLE_ASSERTIONS=On --- Compile with assertion checks enabled (default is Yes for Debug builds, No for all other build types).

    • cmake --build . [-- [options] <target>] or your build system specified above directly.

      • The default target (i.e. ninja or make) will build all of LLVM.

      • The check-all target (i.e. ninja check-all) will run the regression tests to ensure everything is in working order.

      • CMake will generate targets for each tool and library, and most LLVM sub-projects generate their own check-<project> target.

      • Running a serial build will be slow. To improve speed, try running a parallel build. That's done by default in Ninja; for make, use the option -j NNN, where NNN is the number of parallel jobs, e.g. the number of CPUs you have.

    • For more information see CMake

Consult the Getting Started with LLVM page for detailed information on configuring and compiling LLVM. You can visit Directory Layout to learn about the layout of the source code tree.