MdEval: Massively Multilingual Code Debugging

• URL: http://arxiv.org/abs/2411.02310v1
• Date: Mon, 04 Nov 2024 17:36:40 GMT
• Title: MdEval: Massively Multilingual Code Debugging
• Authors: Shukai Liu, Linzheng Chai, Jian Yang, Jiajun Shi, He Zhu, Liran Wang, Ke Jin, Wei Zhang, Hualei Zhu, Shuyue Guo, Tao Sun, Jiaheng Liu, Yunlong Duan, Yu Hao, Liqun Yang, Guanglin Niu, Ge Zhang, Zhoujun Li,
• Abstract summary: We propose the first massively multilingual debug benchmark, which includes 3.6K test samples of 18 programming languages. We introduce the instruction corpora MDEVAL-INSTRUCT by injecting bugs into the correct multilingual queries and solutions. Our experiments on MDEVAL reveal a notable performance gap between open-source models and closed-source LLMs.
• Score: 37.48700033342978
• License:
• Abstract: Code large language models (LLMs) have made significant progress in code debugging by directly generating the correct code based on the buggy code snippet. Programming benchmarks, typically consisting of buggy code snippet and their associated test cases, are used to assess the debugging capabilities of LLMs. However, many existing benchmarks primarily focus on Python and are often limited in terms of language diversity (e.g., DebugBench and DebugEval). To advance the field of multilingual debugging with LLMs, we propose the first massively multilingual debugging benchmark, which includes 3.6K test samples of 18 programming languages and covers the automated program repair (APR) task, the code review (CR) task, and the bug identification (BI) task. Further, we introduce the debugging instruction corpora MDEVAL-INSTRUCT by injecting bugs into the correct multilingual queries and solutions (xDebugGen). Further, a multilingual debugger xDebugCoder trained on MDEVAL-INSTRUCT as a strong baseline specifically to handle the bugs of a wide range of programming languages (e.g. "Missing Mut" in language Rust and "Misused Macro Definition" in language C). Our extensive experiments on MDEVAL reveal a notable performance gap between open-source models and closed-source LLMs (e.g., GPT and Claude series), highlighting huge room for improvement in multilingual code debugging scenarios.

Related papers

• mHumanEval -- A Multilingual Benchmark to Evaluate Large Language Models for Code Generation [28.531581489405745]
  mHumanEval is an extended benchmark supporting prompts in over 200 natural languages. We provide expert human translations for 15 diverse natural languages (NLs) We conclude by analyzing the multilingual code generation capabilities of state-of-the-art (SOTA) Code LLMs.
  arXiv  Detail & Related papers  (2024-10-19T08:44:26Z)
• From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging [5.910272203315325]
  We introduce Multi-Granularity Debugger (MG Debugger), a hierarchical code debugger by isolating, identifying, and resolving bugs at various levels of granularity. MG Debugger decomposes problematic code into a hierarchical tree structure of subfunctions, with each level representing a particular granularity of error. It achieves an 18.9% improvement in accuracy over seed generations in HumanEval and a 97.6% repair success rate in HumanEvalFix.
  arXiv  Detail & Related papers  (2024-10-02T03:57:21Z)
• CRUXEval-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution [50.7413285637879]
  The CRUXEVAL-X code reasoning benchmark contains 19 programming languages. It comprises at least 600 subjects for each language, along with 19K content-consistent tests in total. Even a model trained solely on Python can achieve at most 34.4% Pass@1 in other languages.
  arXiv  Detail & Related papers  (2024-08-23T11:43:00Z)
• Understanding and Mitigating Language Confusion in LLMs [76.96033035093204]
  We evaluate 15 typologically diverse languages with existing and newly-created English and multilingual prompts. We find that Llama Instruct and Mistral models exhibit high degrees of language confusion. We find that language confusion can be partially mitigated via few-shot prompting, multilingual SFT and preference tuning.
  arXiv  Detail & Related papers  (2024-06-28T17:03:51Z)
• DebugBench: Evaluating Debugging Capability of Large Language Models [80.73121177868357]
  DebugBench is a benchmark for Large Language Models (LLMs) It covers four major bug categories and 18 minor types in C++, Java, and Python. We evaluate two commercial and four open-source models in a zero-shot scenario.
  arXiv  Detail & Related papers  (2024-01-09T15:46:38Z)
• Teaching Large Language Models to Self-Debug [62.424077000154945]
  Large language models (LLMs) have achieved impressive performance on code generation. We propose Self- Debugging, which teaches a large language model to debug its predicted program via few-shot demonstrations.
  arXiv  Detail & Related papers  (2023-04-11T10:43:43Z)
• xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval [32.60391966381949]
  We introduce xCodeEval, the largest executable multilingual multitask benchmark to date. It features a total of $7$ tasks involving code understanding, generation, translation and retrieval. xCodeEval adopts an execution-based evaluation and offers a multilingual code execution engine, ExecEval.
  arXiv  Detail & Related papers  (2023-03-06T10:08:51Z)
• X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models [103.75890012041366]
  Language models (LMs) have proven surprisingly successful at capturing factual knowledge. However, studies on LMs' factual representation ability have almost invariably been performed on English. We create a benchmark of cloze-style probes for 23 typologically diverse languages.
  arXiv  Detail & Related papers  (2020-10-13T05:29:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.