{ "info": { "author": null, "author_email": "Emery Berger , Stephen Freund , Kyla Levin , Nicolas van Kempen ", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: Apache Software License", "Operating System :: OS Independent", "Programming Language :: Python :: 3" ], "description": "# ChatDBG\n\nby [Emery Berger](https://emeryberger.com), [Stephen Freund](https://www.cs.williams.edu/~freund/index.html), [Kyla Levin](https://khlevin.github.io/KylaHLevin/index.html), [Nicolas van Kempen](https://nvankempen.com/) (ordered alphabetically)\n\n[![PyPI Latest Release](https://img.shields.io/pypi/v/chatdbg.svg)](https://pypi.org/project/chatdbg/)\n[![Downloads](https://static.pepy.tech/badge/chatdbg)](https://pepy.tech/project/chatdbg)\n[![Downloads](https://static.pepy.tech/badge/chatdbg/month)](https://pepy.tech/project/chatdbg)\n\nChatDBG is an AI-based debugging assistant for C/C++/Python/Rust code that integrates large language models into a standard debugger (`pdb`, `lldb`, `gdb`, and `windbg`) to help debug your code. With ChatDBG, you can engage in a dialog with your debugger, asking open-ended questions about your program, like `why is x null?`. ChatDBG will _take the wheel_ and steer the debugger to answer your queries. ChatDBG can provide error diagnoses and suggest fixes.\n\nAs far as we are aware, ChatDBG is the *first* debugger to automatically perform root cause analysis and to provide suggested fixes.\n\n**Watch ChatDBG in action!**\n| LLDB on [test-overflow.cpp](https://github.com/plasma-umass/ChatDBG/blob/main/samples/cpp/test-overflow.cpp) | GDB on [test-overflow.cpp](https://github.com/plasma-umass/ChatDBG/blob/main/samples/cpp/test-overflow.cpp) | Pdb on [bootstrap.py](https://github.com/plasma-umass/ChatDBG/blob/main/samples/python/bootstrap.py) |\n|:-------------------------:|:-------------------------:|:-------------------------:|\n| | ||\n\nFor technical details and a complete evaluation, see our arXiv paper, [_ChatDBG: An AI-Powered Debugging Assistant_](https://arxiv.org/abs/2403.16354) ([PDF](https://github.com/plasma-umass/ChatDBG/blob/main/ChatDBG-arxiv-2403.16354.pdf)).\n\n> [!NOTE]\n>\n> ChatDBG for `pdb`, `lldb`, and `gdb` are feature-complete; we are currently backporting features for these debuggers into the other debuggers.\n>\n\n## Installation\n\n> [!IMPORTANT]\n>\n> ChatDBG currently needs to be connected to an [OpenAI account](https://openai.com/api/). _Your account will need to have a positive balance for this to work_ ([check your balance](https://platform.openai.com/account/usage)). If you have never purchased credits, you will need to purchase at least \\$1 in credits (if your API account was created before August 13, 2023) or \\$0.50 (if you have a newer API account) in order to have access to GPT-4, which ChatDBG uses. [Get a key here.](https://platform.openai.com/account/api-keys)\n>\n> Once you have an API key, set it as an environment variable called `OPENAI_API_KEY`.\n>\n> ```bash\n> export OPENAI_API_KEY=\n> ```\n\nInstall ChatDBG using `pip` (you need to do this whether you are debugging Python, C, or C++ code):\n\n```bash\npython3 -m pip install chatdbg\n```\n\nIf you are using ChatDBG to debug Python programs, you are done. If you want to use ChatDBG to debug native code with `gdb` or `lldb`, follow the installation instructions below.\n\n### Installing as an `lldb` extension\n\n
\n\nlldb installation instructions\n\n\nInstall ChatDBG into the `lldb` debugger by running the following command:\n\n#### Linux\n\n```bash\npython3 -m pip install ChatDBG\npython3 -c 'import chatdbg; print(f\"command script import {chatdbg.__path__[0]}/chatdbg_lldb.py\")' >> ~/.lldbinit\n```\n\nIf you encounter an error, you may be using an older version of LLVM. Update to the latest version as follows:\n\n```\nsudo apt install -y lsb-release wget software-properties-common gnupg\ncurl -sSf https://apt.llvm.org/llvm.sh | sudo bash -s -- 18 all\n# LLDB now available as `lldb-18`.\n```\n\n#### Mac\n\n```bash\nxcrun python3 -m pip install ChatDBG\nxcrun python3 -c 'import chatdbg; print(f\"command script import {chatdbg.__path__[0]}/chatdbg_lldb.py\")' >> ~/.lldbinit\n```\n\nThis will install ChatDBG as an LLVM extension.\n
\n\n### Installing as a `gdb` extension\n\n
\n\ngdb installation instructions\n\n\nInstall ChatDBG into the `gdb` debugger by running the following command:\n\n```bash\npython3 -m pip install ChatDBG\npython3 -c 'import chatdbg; print(f\"source {chatdbg.__path__[0]}/chatdbg_gdb.py\")' >> ~/.gdbinit\n```\n\nThis will install ChatDBG as a GDB extension.\n
\n\n### Installing as a `WinDBG` extension\n\n
\n\nWinDBG installation instructions\n\n\n1. **Install WinDBG**: Follow instructions [here](https://learn.microsoft.com/en-us/windows-hardware/drivers/debugger/) if `WinDBG` is not installed already.\n1. **Install `vcpkg`**: Follow instructions [here](https://vcpkg.io/en/getting-started) if `vcpkg` is not installed already.\n1. **Install Debugging Tools for Windows**: Install the Windows SDK [here](https://developer.microsoft.com/en-us/windows/downloads/windows-sdk/) and check the box `Debugging Tools for Windows`.\n1. **Navigate to the `src\\chatdbg` directory**: `cd src\\chatdbg`\n1. **Install needed dependencies**: Run`vcpkg install`\n1. **Build the chatdbg.dll extension**: Run`mkdir build & cd build & cmake .. & cmake --build . & cd ..`\n\n**Using ChatDBG**:\n\n * Load into WinDBGX:\n * Run `windbgx your_executable_here.exe`\n * Click the menu items `View` -> `Command browser`\n * Type `.load debug\\chatdbg.dll`\n * After running code and hitting an exception / signal:\n * Type `!why` in Command browser\n
\n\n\n## Usage\n\n### Debugging Python\n\nTo use ChatDBG to debug Python programs, simply run your Python script as follows:\n\n```bash\nchatdbg -c continue yourscript.py\n```\n\nChatDBG is an extension of the standard Python debugger `pdb`. Like\n`pdb`, when your script encounters an uncaught exception, ChatDBG will\nenter post mortem debugging mode.\n\nUnlike other debuggers, you can then use the `why` command to ask\nChatDBG why your program failed and get a suggested fix. After the LLM responds,\nyou may issue additional debugging commands or continue the conversation by entering\nany other text.\n\n#### IPython and Jupyter Support\n\nTo use ChatDBG as the default debugger for IPython or inside Jupyter Notebooks,\ncreate a IPython profile and then add the necessary exensions on startup. (Modify\nthese lines as necessary if you already have a customized profile file.)\n\n```bash\nipython profile create\necho \"c.InteractiveShellApp.extensions = ['chatdbg.chatdbg_pdb', 'ipyflow']\" >> ~/.ipython/profile_default/ipython_config.py\n```\n\nOn the command line, you can then run:\n\n```bash\nipython --pdb yourscript.py\n```\n\nInside Jupyter, run your notebook with the [ipyflow kernel](https://github.com/ipyflow/ipyflow) and include this line magic at the top of the file.\n\n```\n%pdb\n```\n\n\n### Debugging native code (C, C++, or Rust with lldb / gdb)\n\nTo use ChatDBG with `lldb` or `gdb`, just run native code (compiled with `-g` for debugging symbols) with your choice of debugger; when it crashes, ask `why`. This also works for post mortem debugging (when you load a core with the `-c` option). \n\nThe native debuggers work slightly differently than Pdb. After the debugger responds to your question, you will enter into ChatDBG's command loop, as indicated by the `(ChatDBG chatting)` prompt. You may continue issuing debugging commands and you may send additional messages to the LLM by starting those messages with \"chat\". When you are done, type `quit` to return to the debugger's main command loop.\n\n
\n\nDebugging Rust programs\n\n\nTo use ChatDBG with Rust, you need to do two steps: modify your\n`Cargo.toml` file and add one line to your source program.\n\n1. Add this to your `Cargo.toml` file:\n\n```toml\n[dependencies]\nchatdbg = \"0.6.2\"\n\n[profile.dev]\npanic = \"abort\"\n\n[profile.release]\npanic = \"abort\"\n```\n\n2. In your program, apply the `#[chatdbg::main]` attribute to your `main`\nfunction:\n\n```rust\n#[chatdbg::main]\nfn main() {\n```\n\nNow you can debug your Rust code with `gdb` or `lldb`.\n\n
\n\n### Examples\n\n
\n\nChatDBG example in lldb\n\n\n```gdb\n(ChatDBG lldb) run\nProcess 85494 launched: '/Users/emery/git/ChatDBG/test/a.out' (arm64)\nTEST 1\nTEST -422761288\nTEST 0\nTEST 0\nTEST 0\nTEST 0\nTEST 0\nTEST 0\nProcess 85494 stopped\n* thread #1, queue = 'com.apple.main-thread', stop reason = EXC_BAD_ACCESS (code=1, address=0x100056200)\n frame #0: 0x0000000100002f64 a.out`foo(n=8, b=1) at test.cpp:7:22\n 4 int x[] = { 1, 2, 3, 4, 5 };\n 5 \n 6 void foo(int n, float b) {\n-> 7 cout << \"TEST \" << x[n * 10000] << endl;\n 8 }\n 9 \n 10 int main()\nTarget 0: (a.out) stopped.\n```\n\nAsk `why` to have ChatDBG provide a helpful explanation why this program failed, and suggest a fix:\n\n\n```gdb\n(ChatDBG lldb) why\nThe root cause of this error is accessing an index of the array `x`\nthat is out of bounds. In `foo()`, the index is calculated as `n *\n10000`, which can be much larger than the size of the array `x` (which\nis only 5 elements). In the given trace, the program is trying to\naccess the memory address `0x100056200`, which is outside of the range\nof allocated memory for the array `x`.\n\nTo fix this error, we need to ensure that the index is within the\nbounds of the array. One way to do this is to check the value of `n`\nbefore calculating the index, and ensure that it is less than the size\nof the array divided by the size of the element. For example, we can\nmodify `foo()` as follows:\n\n ```\n void foo(int n, float b) {\n if (n < 0 || n >= sizeof(x)/sizeof(x[0])) {\n cout << \"ERROR: Invalid index\" << endl;\n return;\n }\n cout << \"TEST \" << x[n] << endl;\n }\n ```\n\nThis code checks if `n` is within the valid range, and prints an error\nmessage if it is not. If `n` is within the range, the function prints\nthe value of the element at index `n` of `x`. With this modification,\nthe program will avoid accessing memory outside the bounds of the\narray, and will print the expected output for valid indices.\n```\n
\n\n
\n\nChatDBG example in Python (pdb)\n\n\n```python\nTraceback (most recent call last):\n File \"yourscript.py\", line 9, in \n print(tryme(100))\n File \"yourscript.py\", line 4, in tryme\n if x / i > 2:\nZeroDivisionError: division by zero\nUncaught exception. Entering post mortem debugging\nRunning 'cont' or 'step' will restart the program\n> yourscript.py(4)tryme()\n-> if x / i > 2:\n```\n\nAsk `why` to have ChatDBG provide a helpful explanation why this program failed, and suggest a fix:\n\n```python\n(ChatDBG Pdb) why\nThe root cause of the error is that the code is attempting to\ndivide by zero in the line \"if x / i > 2\". As i ranges from 0 to 99,\nit will eventually reach the value of 0, causing a ZeroDivisionError.\n\nA possible fix for this would be to add a check for i being equal to\nzero before performing the division. This could be done by adding an\nadditional conditional statement, such as \"if i == 0: continue\", to\nskip over the iteration when i is zero. The updated code would look\nlike this:\n\ndef tryme(x):\n count = 0\n for i in range(100):\n if i == 0:\n continue\n if x / i > 2:\n count += 1\n return count\n\nif __name__ == '__main__':\n print(tryme(100))\n```\n\n
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