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.

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.

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