How Does Naming Affect LLMs on Code Analysis Tasks?

• URL: http://arxiv.org/abs/2307.12488v5
• Date: Sun, 28 Jul 2024 07:48:44 GMT
• Title: How Does Naming Affect LLMs on Code Analysis Tasks?
• Authors: Zhilong Wang, Lan Zhang, Chen Cao, Nanqing Luo, Xinzhi Luo, Peng Liu,
• Abstract summary: Large Language Models (LLMs) were proposed for natural language processing (NLP) and have shown promising results as general-purpose language models. This paper investigates how naming affects LLMs on code analysis tasks.
• Score: 8.150719423943109
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Large Language Models (LLMs), such as GPT and BERT, were proposed for natural language processing (NLP) and have shown promising results as general-purpose language models. An increasing number of industry professionals and researchers are adopting LLMs for program analysis tasks. However, one significant difference between programming languages and natural languages is that a programmer has the flexibility to assign any names to variables, methods, and functions in the program, whereas a natural language writer does not. Intuitively, the quality of naming in a program affects the performance of LLMs in program analysis tasks. This paper investigates how naming affects LLMs on code analysis tasks. Specifically, we create a set of datasets with code containing nonsense or misleading names for variables, methods, and functions, respectively. We then use well-trained models (CodeBERT) to perform code analysis tasks on these datasets. The experimental results show that naming has a significant impact on the performance of code analysis tasks based on LLMs, indicating that code representation learning based on LLMs heavily relies on well-defined names in code. Additionally, we conduct a case study on some special code analysis tasks using GPT, providing further insights.

Related papers

• Crystal: Illuminating LLM Abilities on Language and Code [58.5467653736537]
  We propose a pretraining strategy to enhance the integration of natural language and coding capabilities. The resulting model, Crystal, demonstrates remarkable capabilities in both domains.
  arXiv  Detail & Related papers  (2024-11-06T10:28:46Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• Source Code Summarization in the Era of Large Language Models [23.715005053430957]
  Large language models (LLMs) have led to a great boost in the performance of code-related tasks. In this paper, we undertake a systematic and comprehensive study on code summarization in the era of LLMs.
  arXiv  Detail & Related papers  (2024-07-09T05:48:42Z)
• Analyzing the Role of Semantic Representations in the Era of Large Language Models [104.18157036880287]
  We investigate the role of semantic representations in the era of large language models (LLMs) We propose an AMR-driven chain-of-thought prompting method, which we call AMRCoT. We find that it is difficult to predict which input examples AMR may help or hurt on, but errors tend to arise with multi-word expressions.
  arXiv  Detail & Related papers  (2024-05-02T17:32:59Z)
• Perplexed: Understanding When Large Language Models are Confused [3.4208414448496027]
  This paper introduces perplexed, a library for exploring where a language model is perplexed. We conducted a case study focused on Large Language Models (LLMs) for code generation using an additional tool we built to help with the analysis of code models called codetokenizer. We found that our studied code LLMs had their worst performance on coding structures where the code was not syntactically correct.
  arXiv  Detail & Related papers  (2024-04-09T22:03:39Z)
• CodecLM: Aligning Language Models with Tailored Synthetic Data [51.59223474427153]
  We introduce CodecLM, a framework for adaptively generating high-quality synthetic data for instruction-following abilities. We first encode seed instructions into metadata, which are concise keywords generated on-the-fly to capture the target instruction distribution. We also introduce Self-Rubrics and Contrastive Filtering during decoding to tailor data-efficient samples.
  arXiv  Detail & Related papers  (2024-04-08T21:15:36Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)
• Exploring Large Language Models for Code Explanation [3.2570216147409514]
  Large Language Models (LLMs) have made remarkable strides in Natural Language Processing. This study specifically delves into the task of generating natural-language summaries for code snippets, using various LLMs.
  arXiv  Detail & Related papers  (2023-10-25T14:38:40Z)
• CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models [43.655927559990616]
  We propose CodeApex, a benchmark dataset focusing on the programming comprehension, code generation, and code correction abilities of LLMs. We evaluate 12 widely used LLMs, including both general-purpose and specialized models. GPT-4 exhibits the best programming capabilities, achieving approximate accuracy of 69%, 54%, and 66% on the three tasks, respectively.
  arXiv  Detail & Related papers  (2023-09-05T04:12:01Z)
• The potential of LLMs for coding with low-resource and domain-specific programming languages [0.0]
  This study focuses on the econometric scripting language named hansl of the open-source software gretl. Our findings suggest that LLMs can be a useful tool for writing, understanding, improving, and documenting gretl code.
  arXiv  Detail & Related papers  (2023-07-24T17:17:13Z)
• LEVER: Learning to Verify Language-to-Code Generation with Execution [64.36459105535]
  We propose LEVER, a simple approach to improve language-to-code generation by learning to verify the generated programs with their execution results. Specifically, we train verifiers to determine whether a program sampled from the LLMs is correct or not based on the natural language input, the program itself and its execution results. LEVER consistently improves over the base code LLMs(4.6% to 10.9% with code-davinci) and achieves new state-of-the-art results on all of them.
  arXiv  Detail & Related papers  (2023-02-16T18:23:22Z)

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.