Classifying Code as Human Authored or GPT-4 Generated: A Study on CodeChef Problems

Konsep Inti

Using code stylometry and machine learning, the study distinguishes between human-authored and GPT-4 generated code with high accuracy, showcasing the potential of this approach.

Abstrak

The study explores the viability of using code stylometry and machine learning to differentiate between human-authored and GPT-4 generated code. By analyzing a dataset from CodeChef, the classifier outperforms baselines with an F1-score and AUC-ROC score of 0.91. The research also evaluates performance across varying levels of problem difficulty, showing promising results in distinguishing between different types of code.

The content discusses the impact of AI assistants like GitHub Copilot and ChatGPT on programming education, focusing on detecting AI-generated solutions in academic settings. It highlights concerns about academic dishonesty due to students submitting AI-generated code as their own work. The study uses a combination of layout, syntactic, lexical features along with Halstead's metrics to build a classifier that can distinguish between human-authored and GPT-4 generated code. Results show high accuracy in identifying GPT-4 generated solutions based on distinctive coding styles.

Statistik

Our classifier achieved an F1-score and AUC-ROC score of 0.91.
A variant excluding gameable features still performed well with an F1-score and AUC-ROC score of 0.89.
Classifier performance varied across different levels of problem difficulty.

Kutipan

"Artificial intelligence (AI) assistants are revolutionizing how programming tasks are performed." - Content
"Our research explores the viability of using code stylometry and machine learning to distinguish between GPT-4 generated and human-authored code." - Content

Wawasan Utama Disaring Dari

Whodunit

by Oseremen Joy... pada arxiv.org 03-08-2024

https://arxiv.org/pdf/2403.04013.pdf

Pertanyaan yang Lebih Dalam

How can educators effectively prevent students from submitting AI-generated code as their own work?

Educators can implement several strategies to deter students from submitting AI-generated code as their own work.

Education on Academic Integrity: Educators should educate students about the importance of academic integrity and the consequences of plagiarism, including using AI-generated code without attribution.

Clear Assignment Instructions: Provide clear instructions for assignments that explicitly state that all submitted work must be original and created by the student themselves.

Unique Problem Sets: Use unique problem sets or customize existing ones to ensure that solutions cannot be easily found online or generated by AI assistants.

Code Reviews and Viva Voce: Conduct code reviews where students explain their solutions orally, allowing educators to assess their understanding and authenticity of the submitted work.

Use Anti-Plagiarism Tools: Utilize anti-plagiarism tools like Turnitin or MOSS to detect similarities between student submissions and online sources, including AI-generated content.

Randomized Testing Environment: Implement randomized testing environments where each student receives a slightly different version of the assignment, making it harder to share answers.

Encourage Personalization in Code Style: Encourage students to personalize their coding style through comments, variable naming conventions, or algorithmic approaches, making it easier to identify individual authorship.

What ethical considerations should be taken into account when using AI assistants in programming education?

When incorporating AI assistants like GitHub Copilot or ChatGPT in programming education, several ethical considerations need attention:

Attribution and Ownership:

Ensure proper attribution if utilizing code snippets suggested by an AI assistant.
Emphasize respect for intellectual property rights and acknowledge contributions appropriately.

Academic Dishonesty:

Educate students on what constitutes academic dishonesty when using these tools.
Clearly define guidelines on how much assistance is permissible from such tools during assignments.

Privacy Concerns:

Safeguard student data privacy when interacting with these platforms.
Inform students about data collection practices associated with using certain AI assistants.

Bias and Fairness:

Be aware of potential biases present in training data used for developing these models.
Ensure fairness in assessments by considering how reliance on such tools may impact grading criteria uniformly across all students.

5 . Transparency:
Promote transparency regarding the use of these tools within educational settings
Disclose any limitations or biases inherent in the technology being utilized
6 . Accountability:
Establish mechanisms for accountability concerning decisions made based on suggestions provided by an artificial intelligence system

How might advancements in AI technology impact traditional methods of assessing programming skills?

Advancements in AI technology are reshaping traditional methods of assessing programming skills:
1 . Automated Evaluation:
With machine learning algorithms capable of evaluating code quality automatically,
Traditional manual assessment processes may become more efficient
2 . Immediate Feedback:
Real-time feedback provided by intelligent systems allows learners
To receive instant guidance on errors or improvements needed
3 . Customized Learning Paths:
Adaptive learning platforms powered by artificial intelligence can tailor
Educational experiences based on individual strengths & weaknesses
4 . Plagiarism Detection :
Advanced algorithms enable better detection capabilities against plagiarised content,
Enhancing integrity standards within educational institutions
5 . Skill Development Tracking :
Data analytics derived from learner interactions with coding tasks help instructors monitor progress,
Identify areas requiring additional support
6 . Ethical Considerations :
The rise of automated assessment raises concerns about fairness & bias mitigation,
Necessitating ongoing evaluation & adjustment protocols