found the source code of many AI papers hard to read, partially because the authors are not in the mindset of software engineers, therefore the code is not aligned to software engineering best practices. How to solve this problem? I want to understand the code so that I can master the concetps in the papers more thourougly through code reading
Reading and fully understanding research code can be challenging, especially when code bases don’t follow traditional software engineering (SE) best practices. Here are some suggestions for getting more out of (often messy) AI research code:
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Organize the Context Before Diving In • Read the paper thoroughly. Make note of the key steps in the algorithm, the architectural components, and the logic flow. Then map these steps to the filenames, modules, and functions you see in the code.
• Identify the main data structures (tensors, neural network modules, etc.). This may involve reviewing the common frameworks used (e.g., PyTorch, TensorFlow, JAX) and making sure you understand the library-specific constructs. -
Restructure or Annotate the Code Yourself • Create an outline or flowchart of the code structure. For instance, describe how data is loaded, how the model is instantiated, where the main training loop occurs, and how metrics are computed.
• Add your own comments or docstrings in a local copy to reflect your understanding of each class or function. This clarifies the logic flow, which is often missing (or too succinct) in research code.
• Consider rewriting small sections (e.g., the training loop) in a more modular style. By rewriting, you not only get a better sense of how the code works, but you also make the code more maintainable for your own experiments. -
Utilize Tools & Debugging Strategies
• Use an IDE or editor with debugging and code-navigation features. For example, set breakpoints in key areas of the code to see how data flows through the functions.
• Employ logging or print statements to confirm your assumptions about shapes, parameter values, or intermediate outputs.
• Use version control systems (e.g., Git). If the research code doesn’t come with it, initialize a local repository to track your changes and make it easier to revert if needed. -
Lean on Documentation & Community
• If the authors provide any README or tutorials, don’t skip them—even if they appear minimal. Sometimes minor notes or instructions reveal key steps or hidden constraints in the code.
• Check whether there’s an official repository from the paper’s authors or from the framework’s community. Frequently, there are official or semi-official re-implementations that follow better practices.
• Look for forks or community-driven code bases that might have cleaned up or refactored the original code. GitHub issues and discussions can often clarify tricky portions of the original release. -
Compare With Established Libraries
• References to well-tested, widely used libraries (e.g., PyTorch Lightning, HuggingFace Transformers, fastai) can give you a sense of how organized AI codebases typically look.
• Comparing the logic you see in the paper’s code with that of a library example can clarify the role of each function and how an “officially engineered” version of that functionality might appear. -
Re-Implement Key Sections if Needed
• If a certain part of the paper’s idea is crucial for your understanding—such as a novel loss function or a custom optimization scheme—try coding it from scratch in a simpler environment or a minimal script.
• Verify your re-implementation on simpler toy data first to confirm correctness. This will also reveal if the original code has any idiosyncrasies, such as special initialization or conditioning steps. -
Experiment and Validate
• Once you’ve annotated or refactored the code, check the results against the paper’s reported metrics. Casual changes to code structure can introduce subtle bugs, so always test if the outputs match the original.
• Adjust hyperparameters incrementally to see how they affect performance, confirming that the code’s behaviors match the paper’s claims. -
Share Improvements Back (If Possible)
• Consider sending a pull request with clearer code structure or better documentation. While it may not always be accepted, you could at least share it as a fork for other researchers who face the same challenges.
• Writing a blog post or short tutorial/hands-on guide about your refactored version can help the community, and it solidifies your understanding.
Overall, the key is to treat research code as a puzzle: dissect it methodically, fill in the missing documentation, and possibly rewrite core parts for clarity. This process itself provides deeper insights and reinforces the concepts from the paper. Over time, you’ll become more comfortable navigating such code bases—and your own restructured versions will serve as valuable references for future projects