Portable ML Models Predict Compiler Inlining Decisions Without Heavy Infrastructure
A new preprint proposes a portable machine learning framework to predict function-inlining decisions in compilers, aiming to replace the need for heavyweight GCC or LLVM infrastructure. Using a CatBoost model trained on 336,938 callsites from 15 C projects, the approach achieves ROC-AUC 0.928 and PR-AUC 0.713 under leave-one-project-out validation. The framework extracts features from source code and emits predictors as ordinary C code, enabling use in lightweight compilers and tools.
Why it matters: This work could allow lightweight compilers and source-to-source tools to benefit from advanced inlining heuristics without depending on full compiler stacks.
Full story at: arXiv Software Engineering ↗