Top Related Projects
A static analyzer for Java, C, C++, and Objective-C
The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.
AddressSanitizer, ThreadSanitizer, MemorySanitizer
A modern, C++-native, test framework for unit-tests, TDD and BDD - using C++14, C++17 and later (C++11 support is in v2.x branch, and C++03 on the Catch1.x branch)
Quick Overview
Bear is a tool that generates a compilation database for Clang-based tools by intercepting build commands. It's designed to work with various build systems and compilers, making it easier to use Clang tools on projects without modifying their build process.
Pros
- Works with multiple build systems (Make, CMake, Ninja, etc.) without configuration
- Supports various compilers (GCC, Clang, Microsoft Visual C++)
- Generates compilation databases for use with Clang-based tools
- Easy to integrate into existing build processes
Cons
- May not capture all compilation commands in complex build systems
- Requires running the build process to generate the compilation database
- Limited to C and C++ projects
- Can add overhead to the build process
Code Examples
Bear is not a code library, but a command-line tool. Therefore, there are no code examples to provide.
Getting Started
To use Bear, follow these steps:
- Install Bear on your system (e.g.,
apt-get install bear
on Ubuntu) - Run your build command prefixed with
bear
, like this:
bear -- make
- This will generate a
compile_commands.json
file in your current directory - Use the generated
compile_commands.json
file with Clang-based tools
For CMake projects, you can use the -DCMAKE_EXPORT_COMPILE_COMMANDS=ON
option instead of Bear:
cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON .
make
This will also generate a compile_commands.json
file without using Bear.
Competitor Comparisons
A static analyzer for Java, C, C++, and Objective-C
Pros of Infer
- Supports multiple programming languages (Java, C/C++, Objective-C)
- More advanced static analysis capabilities, including interprocedural analysis
- Actively maintained by Facebook with regular updates and improvements
Cons of Infer
- Steeper learning curve and more complex setup process
- Requires more system resources due to its comprehensive analysis
- May produce more false positives in certain scenarios
Code Comparison
Bear (compilation database generation):
compilation_database = []
for command in commands:
compilation_database.append({
'directory': os.getcwd(),
'command': command,
'file': get_source_file(command)
})
Infer (running analysis):
infer run -- javac MyClass.java
infer run -- gcc -c example.c
infer run -- xcodebuild -target MyApp
Bear focuses on generating compilation databases for C/C++ projects, which can be used by various tools. Infer, on the other hand, is a standalone static analysis tool that can analyze code directly and provide more in-depth results across multiple languages.
While Bear is simpler to use and integrate into existing build processes, Infer offers more comprehensive analysis capabilities at the cost of increased complexity and resource usage. The choice between the two depends on the specific needs of the project and the desired level of analysis.
The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.
Pros of LLVM Project
- Comprehensive compiler infrastructure with multiple frontends and backends
- Extensive documentation and large community support
- Actively maintained with frequent updates and improvements
Cons of LLVM Project
- Significantly larger and more complex codebase
- Steeper learning curve for newcomers
- Requires more system resources to build and run
Code Comparison
Bear:
int main(int argc, char *argv[])
{
return bear_main(argc, argv);
}
LLVM Project:
int main(int argc, char **argv) {
InitLLVM X(argc, argv);
cl::ParseCommandLineOptions(argc, argv, "llvm system compiler\n");
return !runToolOnCode(new ClangTool, argv[1]);
}
Key Differences
- Bear focuses on generating compilation databases, while LLVM Project is a complete compiler infrastructure
- Bear is a smaller, more specialized tool, whereas LLVM Project offers a wide range of compiler-related functionalities
- Bear is easier to set up and use for specific tasks, while LLVM Project provides more flexibility and power for complex compiler operations
Use Cases
- Bear: Ideal for developers needing to generate compilation databases for existing projects
- LLVM Project: Suitable for compiler developers, researchers, and those working on large-scale code analysis or optimization projects
AddressSanitizer, ThreadSanitizer, MemorySanitizer
Pros of Sanitizers
- Comprehensive suite of dynamic analysis tools for detecting various runtime errors
- Developed and maintained by Google, ensuring high-quality and regular updates
- Supports multiple programming languages, including C, C++, and Go
Cons of Sanitizers
- More complex setup and integration process
- May introduce performance overhead during runtime analysis
- Requires recompilation of the codebase with specific flags
Code Comparison
Bear:
#include "bear/Bear.h"
int main() {
bear::Bear bear;
bear.run();
return 0;
}
Sanitizers:
#include <sanitizer/asan_interface.h>
int main() {
char *x = new char[10];
delete[] x;
return __asan_address_is_poisoned(x);
}
Summary
Sanitizers offers a robust set of tools for detecting runtime errors across multiple languages, backed by Google's development resources. However, it may require more setup and potentially impact performance. Bear, on the other hand, focuses on build command interception and is generally simpler to use but has a more specific use case. The choice between the two depends on the project's needs and the level of runtime analysis required.
A modern, C++-native, test framework for unit-tests, TDD and BDD - using C++14, C++17 and later (C++11 support is in v2.x branch, and C++03 on the Catch1.x branch)
Pros of Catch2
- Header-only library, making it easy to integrate into projects
- Extensive test case and assertion macros for flexible testing
- Built-in command-line parser for test filtering and reporting options
Cons of Catch2
- Slower compile times due to its header-only nature
- Steeper learning curve for advanced features and customizations
Code Comparison
Catch2 test case example:
TEST_CASE("Addition works", "[math]") {
REQUIRE(1 + 1 == 2);
REQUIRE(2 + 2 == 4);
}
Bear usage example:
bear -- g++ -c main.cpp
Key Differences
- Bear is a build intercept tool for generating compilation databases, while Catch2 is a unit testing framework
- Bear focuses on capturing compiler commands, whereas Catch2 provides a framework for writing and running tests
- Bear is primarily used in the build process, while Catch2 is used in the testing phase of development
Use Cases
- Use Bear when you need to generate a compilation database for tools like clang-tidy or IDE integrations
- Choose Catch2 when you want a modern, feature-rich unit testing framework for C++ projects
Community and Support
- Catch2 has a larger community and more frequent updates
- Bear has fewer contributors but is actively maintained for its specific use case
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ÊÂ·á´¥Â·Ê Build EAR
Bear is a tool that generates a compilation database for clang tooling.
The JSON compilation database is used in the clang project to provide information on how a single compilation unit is processed. With this, it is easy to re-run the compilation with alternate programs.
Some build system natively supports the generation of JSON compilation database. For projects which does not use such build tool, Bear generates the JSON file during the build process.
How to install
Bear is packaged for many distributions. Check out your package manager. Or build it from source.
How to use
After installation the usage is like this:
bear -- <your-build-command>
The output file called compile_commands.json
is saved in the current directory.
For more options you can check the man page or pass --help
parameter. Note
that if you want to pass parameter to Bear, pass those before the --
sign,
everything after that will be the build command.
Please be aware that some package manager still ship our old 2.4.x release.
In that case please omit the extra --
sign or consult your local documentation.
For more, read the man pages or wiki of the project, which talks about limitations, known issues and platform specific usage.
Problem reports
Before you open a new problem report, please look at the wiki if your problem is a known one with documented workaround. It's also helpful to look at older (maybe closed) issues before you open a new one.
If you decided to report a problem, try to give as much context as it would help me to reproduce the error you see. If you just have a question about the usage, please don't be shy, ask your question in an issue or in chat.
If you found a bug, but also found a fix for it, please share it with me and open a pull request.
Please follow the contribution guide when you do these.
Thanks to JetBrains for donating product licenses to help develop Bear
Top Related Projects
A static analyzer for Java, C, C++, and Objective-C
The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.
AddressSanitizer, ThreadSanitizer, MemorySanitizer
A modern, C++-native, test framework for unit-tests, TDD and BDD - using C++14, C++17 and later (C++11 support is in v2.x branch, and C++03 on the Catch1.x branch)
Convert designs to code with AI
Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.
Try Visual Copilot