TCProF: Time-Complexity Prediction SSL Framework

TL;DR

TCProF is a novel semi-supervised framework designed to predict code time complexity effectively in low-resource scenarios, outperforming existing approaches by over 60%.

Contribution

It introduces the first SSL framework for code time complexity prediction tailored for low-resource settings, combining augmentation, symbolic modules, and co-training.

Findings

Achieves over 60% improvement over self-training methods.

Outperforms ChatGPT and Gemini-Pro in complexity prediction.

Provides extensive comparative analysis of approaches.

Abstract

Time complexity is a theoretic measure to determine the amount of time the algorithm needs for its execution. In reality, developers write algorithms into code snippets within limited resources, making the calculation of a code's time complexity a fundamental task. However, determining the precise time complexity of a code is theoretically undecidable. In response, recent advancements have leaned toward deploying datasets for code time complexity prediction and initiating preliminary experiments for this challenge. We investigate the challenge in low-resource scenarios where only a few labeled instances are given for training. Remarkably, we are the first to introduce TCProF: a Time-Complexity Prediction SSL Framework as an effective solution for code time complexity prediction in low-resource settings. TCProF significantly boosts performance by integrating our augmentation, symbolic modules, and a co-training mechanism, achieving a more than 60% improvement over self-training approaches. We further provide an extensive comparative analysis between TCProF, ChatGPT, and Gemini-Pro, offering a detailed evaluation of our approach. Our code is at https://github.com/peer0/few-shot-tc.