CoderUJB: An Executable and Unified Java Benchmark for Practical Programming Scenarios

• URL: http://arxiv.org/abs/2403.19287v1
• Date: Thu, 28 Mar 2024 10:19:18 GMT
• Title: CoderUJB: An Executable and Unified Java Benchmark for Practical Programming Scenarios
• Authors: Zhengran Zeng, Yidong Wang, Rui Xie, Wei Ye, Shikun Zhang,
• Abstract summary: We introduce CoderUJB, a new benchmark designed to evaluate large language models (LLMs) across diverse Java programming tasks. Our empirical study on this benchmark investigates the coding abilities of various open-source and closed-source LLMs. The findings indicate that while LLMs exhibit strong potential, challenges remain, particularly in non-functional code generation.
• Score: 25.085449990951034
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the evolving landscape of large language models (LLMs) tailored for software engineering, the need for benchmarks that accurately reflect real-world development scenarios is paramount. Current benchmarks are either too simplistic or fail to capture the multi-tasking nature of software development. To address this, we introduce CoderUJB, a new benchmark designed to evaluate LLMs across diverse Java programming tasks that are executable and reflective of actual development scenarios, acknowledging Java's prevalence in real-world software production. CoderUJB comprises 2,239 programming questions derived from 17 real open-source Java projects and spans five practical programming tasks. Our empirical study on this benchmark investigates the coding abilities of various open-source and closed-source LLMs, examining the effects of continued pre-training in specific programming languages code and instruction fine-tuning on their performance. The findings indicate that while LLMs exhibit strong potential, challenges remain, particularly in non-functional code generation (e.g., test generation and defect detection). Importantly, our results advise caution in the specific programming languages continued pre-training and instruction fine-tuning, as these techniques could hinder model performance on certain tasks, suggesting the need for more nuanced strategies. CoderUJB thus marks a significant step towards more realistic evaluations of programming capabilities in LLMs, and our study provides valuable insights for the future development of these models in software engineering.

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