Machine Learning-Guided Inlining for LLVM
Inline-ML is a research prototype that applies machine learning to LLVM IR inlining decisions. Originally created for a capstone project, it now serves as a standalone CLI tool that explores data-driven compiler optimization. The system learns from real-world C codebases to predict when inlining should occur, using interpretable models like XGBoost.