Large Language Models for Code Analysis: Do LLMs Really Do Their Job?
- URL: http://arxiv.org/abs/2310.12357v2
- Date: Tue, 5 Mar 2024 23:30:14 GMT
- Title: Large Language Models for Code Analysis: Do LLMs Really Do Their Job?
- Authors: Chongzhou Fang, Ning Miao, Shaurya Srivastav, Jialin Liu, Ruoyu Zhang,
Ruijie Fang, Asmita, Ryan Tsang, Najmeh Nazari, Han Wang and Houman Homayoun
- Abstract summary: Large language models (LLMs) have demonstrated significant potential in the realm of natural language understanding and programming code processing tasks.
This paper offers a comprehensive evaluation of LLMs' capabilities in performing code analysis tasks.
- Score: 13.48555476110316
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models (LLMs) have demonstrated significant potential in the
realm of natural language understanding and programming code processing tasks.
Their capacity to comprehend and generate human-like code has spurred research
into harnessing LLMs for code analysis purposes. However, the existing body of
literature falls short in delivering a systematic evaluation and assessment of
LLMs' effectiveness in code analysis, particularly in the context of obfuscated
code.
This paper seeks to bridge this gap by offering a comprehensive evaluation of
LLMs' capabilities in performing code analysis tasks. Additionally, it presents
real-world case studies that employ LLMs for code analysis. Our findings
indicate that LLMs can indeed serve as valuable tools for automating code
analysis, albeit with certain limitations. Through meticulous exploration, this
research contributes to a deeper understanding of the potential and constraints
associated with utilizing LLMs in code analysis, paving the way for enhanced
applications in this critical domain.
Related papers
- Analysis on LLMs Performance for Code Summarization [0.0]
Large Language Models (LLMs) have significantly advanced the field of code summarization.
This study aims to perform a comparative analysis of several open-source LLMs, namely LLaMA-3, Phi-3, Mistral, and Gemma.
arXiv Detail & Related papers (2024-12-22T17:09:34Z) - What You See Is Not Always What You Get: An Empirical Study of Code Comprehension by Large Language Models [0.5735035463793009]
We investigate the vulnerability of large language models (LLMs) to imperceptible attacks, where hidden character manipulation in source code misleads LLMs' behaviour while remaining undetectable to human reviewers.
These attacks include coding reordering, invisible coding characters, code deletions, and code homoglyphs.
Our findings confirm the susceptibility of LLMs to imperceptible coding character attacks, while different LLMs present different negative correlations between perturbation magnitude and performance.
arXiv Detail & Related papers (2024-12-11T04:52:41Z) - Codellm-Devkit: A Framework for Contextualizing Code LLMs with Program Analysis Insights [9.414198519543564]
We present codellm-devkit (hereafter, CLDK'), an open-source library that significantly simplifies the process of performing program analysis.
CLDK offers developers an intuitive and user-friendly interface, making it incredibly easy to provide rich program analysis context to code LLMs.
arXiv Detail & Related papers (2024-10-16T20:05:59Z) - What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [80.18342600996601]
Large language models (LLMs) produce code that is shorter yet more complicated as compared to canonical solutions.
We develop a taxonomy of bugs for incorrect codes that includes three categories and 12 sub-categories, and analyze the root cause for common bug types.
We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code based on bug types and compiler feedback.
arXiv Detail & Related papers (2024-07-08T17:27:17Z) - FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition [56.76951887823882]
Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks.
We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
arXiv Detail & Related papers (2024-02-29T21:05:37Z) - LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges.
Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model.
This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
arXiv Detail & Related papers (2024-02-26T07:33:05Z) - Rethinking Interpretability in the Era of Large Language Models [76.1947554386879]
Large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks.
The capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human.
These new capabilities raise new challenges, such as hallucinated explanations and immense computational costs.
arXiv Detail & Related papers (2024-01-30T17:38:54Z) - 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) - 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) - Sentiment Analysis in the Era of Large Language Models: A Reality Check [69.97942065617664]
This paper investigates the capabilities of large language models (LLMs) in performing various sentiment analysis tasks.
We evaluate performance across 13 tasks on 26 datasets and compare the results against small language models (SLMs) trained on domain-specific datasets.
arXiv Detail & Related papers (2023-05-24T10:45:25Z) - LMs: Understanding Code Syntax and Semantics for Code Analysis [25.508254718438636]
We evaluate the capabilities of large language models (LLMs) and their limitations for code analysis in software engineering.
We employ four state-of-the-art foundational models, GPT4, GPT3.5, StarCoder and CodeLlama-13b-instruct.
arXiv Detail & Related papers (2023-05-20T08:43:49Z)
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.