Coding problems are problems that require a solution in the form of a
computer program. Coding problems are popular among students and professionals
as it enhances their skills and career opportunities. An AI system that would
help those who practice coding problems would be highly useful and there is a
huge potential for such a system. In this work, we propose a model which uses
stacking of hyperparameter tuned boosting models to achieve impressive metric
scores of 77.8% accuracy and 0.815 PR-AUC on the dataset that was scraped from
Codeforces and Leetcode. We open source the dataset and the models developed
for this work.

本文提出使用超参数调节提高准确率的增强模型堆叠来帮助编码问题的练习，取得了 77.8％的准确率和 0.815 的 PR-AUC，并公开了数据集和模型。

使用超参数调整的模型叠加技术进行编码问题标注

Stacking of Hyperparameter Tuned Models for Tagging Coding Problems

In the burgeoning field of artificial intelligence (AI), understanding the
capabilities and limitations of programming-oriented models is crucial. This
paper presents a novel evaluation of the programming proficiency of Generative
Pretrained Transformer (GPT) models, specifically GPT-3.5 and GPT-4, against
coding problems of varying difficulty levels drawn from Codewars. The
experiments reveal a distinct boundary at the 3kyu level, beyond which these
GPT models struggle to provide solutions. These findings led to the proposal of
a measure for coding problem complexity that incorporates both problem
difficulty and the time required for solution. The research emphasizes the need
for validation and creative thinking capabilities in AI models to better
emulate human problem-solving techniques. Future work aims to refine this
proposed complexity measure, enhance AI models with these suggested
capabilities, and develop an objective measure for programming problem
difficulty. The results of this research offer invaluable insights for
improving AI programming capabilities and advancing the frontier of AI
problem-solving abilities.

本研究评估了 GPT-3.5 和 GPT-4 编程模型的编程熟练度，这些模型对于不同难度级别的编程问题的求解存在明显的局限性，作者提出了一个融合了问题难度和解题时间的编程问题复杂性度量标准，并强调了需要在 AI 模型中加入验证和创造性思维能力以更好地模拟人类问题解决技术的必要性。