# GFTrans: an on-the-fly static analysis framework for code performance profiling

## TL;DR

GFTrans is a new static analysis tool that predicts C program performance without running the code, helping developers find and fix bottlenecks quickly.

## Contribution

GFTrans introduces a novel static analysis framework using anchor-based embeddings and a dynamic gating mechanism for performance prediction.

## Key findings

- GFTrans achieves 78.64% accuracy in predicting C code performance, outperforming baselines like Random Forest and Code2Vec.
- The framework identifies performance bottlenecks in milliseconds, enabling real-time optimization during coding.
- Anchor-based embeddings and dynamic gating effectively capture code complexity by integrating control flow and data dependencies.

## Abstract

Improving software efficiency is crucial for maintenance, but pinpointing runtime bottlenecks becomes increasingly difficult as systems expand. Traditional dynamic profiling tools require full build-execution cycles, creating significant latency that impedes agile development. To address this, we introduce GFTrans, a static analysis framework that predicts c program performance without execution. GFTrans utilizes a Transformer architecture with a novel “anchor-based embedding” technique to integrate control flow and data dependencies into a unified sequence. Additionally, a dynamic gating mechanism fuses these semantic representations with 16 handcrafted statistical features to comprehensively capture code complexity. Evaluated on a dataset of real-world GitHub c functions with high-precision runtime labels, GFTrans outperforms baseline models like Random Forest and Code2Vec, achieving 78.64% accuracy. The system identifies potential bottlenecks in milliseconds, enabling developers to perform optimization effectively during the coding phase.