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Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study

https://doi.org/10.1145/3716848
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-84800-044-5_11openalex-snapshot
Selecting Empirical Methods for Software Engineering Research
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Static Analysis for Proactive Security
inferredcites →https://doi.org/10.1007/s10664-008-9102-8openalex-snapshot
Guidelines for conducting and reporting case study research in software engineering
inferredcites →https://doi.org/10.1007/s10664-023-10380-1openalex-snapshot
Is GitHub’s Copilot as bad as humans at introducing vulnerabilities in code?
inferredcites →https://doi.org/10.1007/s40279-017-0824-xopenalex-snapshot
Comment on: “Comparison of Periodized and Non-Periodized Resistance Training on Maximal Strength: A Meta-Analysis”
inferredcites →https://doi.org/10.1016/j.jss.2023.111734openalex-snapshot
GitHub Copilot AI pair programmer: Asset or Liability?
inferredcites →https://doi.org/10.1016/j.procs.2020.04.217openalex-snapshot
A Comparative Study of Static Code Analysis tools for Vulnerability Detection in C/C++ and JAVA Source Code
inferredcites →https://doi.org/10.1109/access.2017.2682323openalex-snapshot
A Systematic Mapping Study of Software Development With GitHub
inferredcites →https://doi.org/10.1109/aicai.2019.8701341openalex-snapshot
Python for Data Analytics, Scientific and Technical Applications
inferredcites →https://doi.org/10.1109/apsec66846.2025.00047openalex-snapshot
Where Is Self-admitted Code Generated by Large Language Models on GitHub?
inferredcites →https://doi.org/10.1109/eurosp57164.2023.00036openalex-snapshot
Finding Fixed Vulnerabilities with Off-the-Shelf Static Analysis
inferredcites →https://doi.org/10.1109/icse48619.2023.00110openalex-snapshot
CCTEST: Testing and Repairing Code Completion Systems
inferredcites →https://doi.org/10.1109/icsm.2012.6405289openalex-snapshot
A systematic mapping study on dynamic metrics and software quality
inferredcites →https://doi.org/10.1109/iwsc55060.2022.00010openalex-snapshot
An Empirical Evaluation of Competitive Programming AI: A Case Study of AlphaCode
inferredcites →https://doi.org/10.1109/msec.2024.3355713openalex-snapshot
AI Code Generators for Security: Friend or Foe?
inferredcites →https://doi.org/10.1109/msr59073.2023.00084openalex-snapshot
LLMSecEval: A Dataset of Natural Language Prompts for Security Evaluations
inferredcites →https://doi.org/10.1109/saner60148.2024.00051openalex-snapshot
Assessing the Security of GitHub Copilot's Generated Code - A Targeted Replication Study
inferredcites →https://doi.org/10.1109/scam55253.2022.00014openalex-snapshot
An Empirical Study of Code Smells in Transformer-based Code Generation Techniques
inferredcites →https://doi.org/10.1109/smc53992.2023.10394237openalex-snapshot
How Secure is Code Generated by ChatGPT?
inferredcites →https://doi.org/10.1109/sp46214.2022.9833571openalex-snapshot
Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions
inferredcites →https://doi.org/10.1109/tse.2018.2871058openalex-snapshot
The Adoption of JavaScript Linters in Practice: A Case Study on ESLint
inferredcites →https://doi.org/10.1109/tse.2020.3004525openalex-snapshot
Why Do Software Developers Use Static Analysis Tools? A User-Centered Study of Developer Needs and Motivations
inferredcites →https://doi.org/10.1109/tse.2022.3140868openalex-snapshot
The Secret Life of Software Vulnerabilities: A Large-Scale Empirical Study
inferredcites →https://doi.org/10.1126/science.abq1158openalex-snapshot
Competition-level code generation with AlphaCode
inferredcites →https://doi.org/10.1142/s0218194023410048openalex-snapshot
Demystifying Practices, Challenges and Expected Features of Using GitHub Copilot
inferredcites →https://doi.org/10.1145/3188720openalex-snapshot
Lessons from building static analysis tools at Google
inferredcites →https://doi.org/10.1145/3338112openalex-snapshot
Scaling static analyses at Facebook
inferredcites →https://doi.org/10.1145/3377811.3380441openalex-snapshot
On the recall of static call graph construction in practice
inferredcites →https://doi.org/10.1145/3491101.3519665openalex-snapshot
Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models
inferredcites →https://doi.org/10.1145/3510003.3510214openalex-snapshot
Detecting false alarms from automatic static analysis tools
inferredcites →https://doi.org/10.1145/3512290.3528700openalex-snapshot
Choose your programming copilot
inferredcites →https://doi.org/10.1145/3524842.3528470openalex-snapshot
An empirical evaluation of GitHub copilot's code suggestions
inferredcites →https://doi.org/10.1145/3545945.3569759openalex-snapshot
Programming Is Hard - Or at Least It Used to Be
inferredcites →https://doi.org/10.1145/3549035.3561184openalex-snapshot
SecurityEval dataset: mining vulnerability examples to evaluate machine learning-based code generation techniques
inferredcites →https://doi.org/10.1145/3576915.3623157openalex-snapshot
Do Users Write More Insecure Code with AI Assistants?
inferredcites →https://doi.org/10.1145/3576915.3623175openalex-snapshot
Large Language Models for Code: Security Hardening and Adversarial Testing
inferredcites →https://doi.org/10.1145/3586030openalex-snapshot
Grounded Copilot: How Programmers Interact with Code-Generating Models
inferredcites →https://doi.org/10.1145/3613904.3641936openalex-snapshot
Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming
inferredcites →https://doi.org/10.1145/3643674openalex-snapshot
Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues
inferredcites →https://doi.org/10.1145/3748647openalex-snapshot
Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey
inferredcites →https://doi.org/10.1177/001316446002000104openalex-snapshot
A Coefficient of Agreement for Nominal Scales
inferredcites →https://doi.org/10.1177/0049124113500475openalex-snapshot
Coding In-depth Semistructured Interviews
inferredcites →https://doi.org/10.3929/ethz-b-000720060openalex-snapshot
Instruction Tuning for Secure Code Generation
inferredcites →https://doi.org/10.4230/lipics.esem.2026.34openalex-snapshot
A Direction-Aware Study of LLM-Based Code Translation Across Eight Programming Languages
inferredcites →https://doi.org/10.48550/arxiv.2208.06213openalex-snapshot
What is it like to program with artificial intelligence?
inferredcites →https://doi.org/10.48550/arxiv.2208.09727openalex-snapshot
Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants
inferredcites →https://doi.org/10.48550/arxiv.2209.01766openalex-snapshot
Exploring the Verifiability of Code Generated by GitHub Copilot
inferredcites →https://doi.org/10.48550/arxiv.2302.11382openalex-snapshot
A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
inferredcites →https://doi.org/10.48550/arxiv.2303.04142openalex-snapshot
From Copilot to Pilot: Towards AI Supported Software Development
inferredcites →https://doi.org/10.48550/arxiv.2304.10778openalex-snapshot
Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT
inferredcites →https://doi.org/10.48550/arxiv.2402.00689openalex-snapshot
Ocassionally Secure: A Comparative Analysis of Code Generation Assistants
inferredcites →https://doi.org/10.5281/zenodo.10802054openalex-snapshot
Dataset of the Paper: Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study

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How Secure are Full-Stack Web Applications Generated by Large Language Models? A Multi-language Security Study
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Security Vulnerabilities in AI-Generated Code: A Large-Scale Analysis of Public GitHub Repositories
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Performance analysis of AI-generated code: A case study of Copilot, Copilot Chat, CodeLlaMa, and DeepSeek-Coder models
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Developers and generative AI: A study of self-admitted usage in open source projects
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Cybersecurity Behavior in AI Vibe Coding: Extending Technology Threat Avoidance Theory
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CFCEval: Evaluating Security Aspects in Code Generated by Large Language Models
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