OOP

The OOP benchmark features 431 Python programs that encompass essential OOP concepts and features like classes and encapsulation methods¹². The authors of the paper argue that current evaluation frameworks largely neglect OOP in favor of functional programming (FP), such as HumanEval and MBPP¹. To address this, they introduced this OOP-focused benchmark¹².

In addition to the benchmark, they also propose a novel evaluation metric, pass@o, tailored for OOP, enhancing traditional pass@k measures¹². This metric offers a more relevant and comprehensive assessment for OOP code generation¹.

The evaluation of 23 leading large language models (LLMs), including both general and code-specialized models, reveals three key insights¹²: 1) pass@o offers a more relevant and comprehensive assessment for OOP code generation¹². 2) Despite excelling in FP, code-specialized LLMs like WizardCoder lag in OOP compared to models like ChatGPT¹². 3) The poor performance of all advanced LLMs on the OOP benchmark highlights a critical need for improvements in this field¹².

(1) OOP: Object-Oriented Programming Evaluation Benchmark for Large .... https://arxiv.org/abs/2401.06628. (2) OOP: Object-Oriented Programming Evaluation Benchmark. https://arxiv.org/html/2401.06628v2. (3) OOP: Object-Oriented Programming Evaluation Benchmark .... https://www.x-mol.com/paper/1747067589511319552. (4) OOP：大型语言模型的面向对象编程评估基准,arXiv - CS .... https://www.x-mol.com/paper/1747067589511319552/t. (5) undefined. https://doi.org/10.48550/arXiv.2401.06628. (6) undefined. https://github.com/alphadl/OOP-eval.