Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human Programmers

• URL: http://arxiv.org/abs/2401.06461v4
• Date: Mon, 8 Jul 2024 08:45:55 GMT
• Title: Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human Programmers
• Authors: Yuling Shi, Hongyu Zhang, Chengcheng Wan, Xiaodong Gu,
• Abstract summary: We study the specific patterns that characterize machine- and human-authored code. We propose DetectCodeGPT, a novel method for detecting machine-generated code.
• Score: 14.018844722021896
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models have catalyzed an unprecedented wave in code generation. While achieving significant advances, they blur the distinctions between machine- and human-authored source code, causing integrity and authenticity issues of software artifacts. Previous methods such as DetectGPT have proven effective in discerning machine-generated texts, but they do not identify and harness the unique patterns of machine-generated code. Thus, its applicability falters when applied to code. In this paper, we carefully study the specific patterns that characterize machine- and human-authored code. Through a rigorous analysis of code attributes such as lexical diversity, conciseness, and naturalness, we expose unique patterns inherent to each source. We particularly notice that the syntactic segmentation of code is a critical factor in identifying its provenance. Based on our findings, we propose DetectCodeGPT, a novel method for detecting machine-generated code, which improves DetectGPT by capturing the distinct stylized patterns of code. Diverging from conventional techniques that depend on external LLMs for perturbations, DetectCodeGPT perturbs the code corpus by strategically inserting spaces and newlines, ensuring both efficacy and efficiency. Experiment results show that our approach significantly outperforms state-of-the-art techniques in detecting machine-generated code.

Related papers

• Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting [78.48355455324688]
  We propose a novel zero-shot synthetic code detector based on the similarity between the code and its rewritten variants. Our results demonstrate a notable enhancement over existing synthetic content detectors designed for general texts.
  arXiv  Detail & Related papers  (2024-05-25T08:57:28Z)
• CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code [59.32609948217718]
  We present CodeIP, a new watermarking technique for Large Language Models (LLMs)-based code generation. CodeIP enables the insertion of multi-bit information while preserving the semantics of the generated code.
  arXiv  Detail & Related papers  (2024-04-24T04:25:04Z)
• Assaying on the Robustness of Zero-Shot Machine-Generated Text Detectors [57.7003399760813]
  We explore advanced Large Language Models (LLMs) and their specialized variants, contributing to this field in several ways. We uncover a significant correlation between topics and detection performance. These investigations shed light on the adaptability and robustness of these detection methods across diverse topics.
  arXiv  Detail & Related papers  (2023-12-20T10:53:53Z)
• Zero-Shot Detection of Machine-Generated Codes [83.0342513054389]
  This work proposes a training-free approach for the detection of LLMs-generated codes. We find that existing training-based or zero-shot text detectors are ineffective in detecting code. Our method exhibits robustness against revision attacks and generalizes well to Java codes.
  arXiv  Detail & Related papers  (2023-10-08T10:08:21Z)
• Discriminating Human-authored from ChatGPT-Generated Code Via Discernable Feature Analysis [2.9398911304923447]
  This paper specifically aims to distinguish code generated by ChatGPT from that authored by humans. We devise a dataset cleansing technique, which employs temporal and spatial segmentation, to mitigate the dearth of datasets. To further enrich data resources, we employ "code transformation," "feature transformation," and "feature customization" techniques, generating an extensive dataset comprising 10,000 lines of ChatGPT-generated code.
  arXiv  Detail & Related papers  (2023-06-26T03:15:06Z)
• Who Wrote this Code? Watermarking for Code Generation [53.24895162874416]
  We propose Selective WatErmarking via Entropy Thresholding (SWEET) to detect machine-generated text. Our experiments show that SWEET significantly improves code quality preservation while outperforming all baselines.
  arXiv  Detail & Related papers  (2023-05-24T11:49:52Z)
• Can AI-Generated Text be Reliably Detected? [54.670136179857344]
  Unregulated use of LLMs can potentially lead to malicious consequences such as plagiarism, generating fake news, spamming, etc. Recent works attempt to tackle this problem either using certain model signatures present in the generated text outputs or by applying watermarking techniques. In this paper, we show that these detectors are not reliable in practical scenarios.
  arXiv  Detail & Related papers  (2023-03-17T17:53:19Z)
• A Simple, Yet Effective Approach to Finding Biases in Code Generation [16.094062131137722]
  This work shows that current code generation systems exhibit undesired biases inherited from their large language model backbones. We propose the "block of influence" concept, which enables a modular decomposition and analysis of the coding challenges.
  arXiv  Detail & Related papers  (2022-10-31T15:06:15Z)

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.