# No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPT

arXiv: https://arxiv.org/abs/2308.04838 · HTML: https://mlsanity.com/paper/2308.04838/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2023.

## Built on by (most cited first, 30 of 47)

- [Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code](https://mlsanity.com/paper/2404.00971/) (2024)
- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub](https://mlsanity.com/paper/2509.14745/) (2025)
- [An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection](https://mlsanity.com/paper/2406.06822/) (2024)
- [HumanEvalComm: Benchmarking the Communication Competence of Code Generation for LLMs and LLM Agent](https://mlsanity.com/paper/2406.00215/) (2024)
- [Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projects](https://mlsanity.com/paper/2511.04427/) (2025)
- [On the Impacts of Contexts on Repository-Level Code Generation](https://mlsanity.com/paper/2406.11927/) (2024)
- [Evaluating Software Development Agents: Patch Patterns, Code Quality, and Issue Complexity in Real-World GitHub Scenarios](https://mlsanity.com/paper/2410.12468/) (2024)
- [Correctness Assessment of Code Generated by Large Language Models Using Internal Representations](https://mlsanity.com/paper/2501.12934/) (2025)
- [Beyond Final Code: A Process-Oriented Error Analysis of Software Development Agents in Real-World GitHub Scenarios](https://mlsanity.com/paper/2503.12374/) (2025)
- [Just another copy and paste? Comparing the security vulnerabilities of ChatGPT generated code and StackOverflow answers](https://mlsanity.com/paper/2403.15600/) (2024)
- [LLMs: A Game-Changer for Software Engineers?](https://mlsanity.com/paper/2411.00932/) (2024)
- [From Vulnerabilities to Remediation: A Systematic Literature Review of LLMs in Code Security](https://mlsanity.com/paper/2412.15004/) (2024)
- [SandboxEval: Towards Securing Test Environment for Untrusted Code](https://mlsanity.com/paper/2504.00018/) (2025)
- [Generative AI and Empirical Software Engineering: A Paradigm Shift](https://mlsanity.com/paper/2502.08108/) (2025)
- [Risk Assessment Framework for Code LLMs via Leveraging Internal States](https://mlsanity.com/paper/2504.14640/) (2025)
- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [How Well Do Large Language Models Serve as End-to-End Secure Code Agents for Python?](https://mlsanity.com/paper/2408.10495/) (2024)
- [DynaCode: A Dynamic Complexity-Aware Code Benchmark for Evaluating Large Language Models in Code Generation](https://mlsanity.com/paper/2503.10452/) (2025)
- [Can You Really Trust Code Copilots? Evaluating Large Language Models from a Code Security Perspective](https://mlsanity.com/paper/2505.10494/) (2025)
- [Large Language Models Should Ask Clarifying Questions to Increase Confidence in Generated Code](https://mlsanity.com/paper/2308.13507/) (2023)
- [Defending Code Language Models against Backdoor Attacks with Deceptive Cross-Entropy Loss](https://mlsanity.com/paper/2407.08956/) (2024)
- [SwingArena: Competitive Programming Arena for Long-context GitHub Issue Solving](https://mlsanity.com/paper/2505.23932/) (2025)
- [LLM4TDD: Best Practices for Test Driven Development Using Large Language Models](https://mlsanity.com/paper/2312.04687/) (2023)
- [Questionable practices in machine learning](https://mlsanity.com/paper/2407.12220/) (2024)
- [Evaluating the Effectiveness of LLMs in Fixing Maintainability Issues in Real-World Projects](https://mlsanity.com/paper/2502.02368/) (2025)
- [One Step at a Time: Combining LLMs and Static Analysis to Generate Next-Step Hints for Programming Tasks](https://mlsanity.com/paper/2410.09268/) (2024)
- [A Qualitative Study on Using ChatGPT for Software Security: Perception vs. Practicality](https://mlsanity.com/paper/2408.00435/) (2024)
- [Secure-Instruct: An Automated Pipeline for Synthesizing Instruction-Tuning Datasets Using LLMs for Secure Code Generation](https://mlsanity.com/paper/2510.07189/) (2025)

## Abstract (arXiv)

Large language models (LLMs) have demonstrated impressive capabilities across various NLP tasks. Additionally, LLMs are also highly valuable in supporting software engineering tasks, particularly in the field of code generation. Automatic code generation is a process of automatically generating source code or executable code based on given specifications or requirements, improving developer productivity. In this study, we perform a systematic empirical assessment to the quality of code generation using ChatGPT. We leverage 728 algorithm problems in five languages (i.e., C, C++, Java, Python, and JavaScript) and 18 CWEs with 54 code scenarios for the code generation task. Our evaluation encompasses a comprehensive analysis of code snippets generated by ChatGPT, focusing on three critical aspects: correctness, complexity, and security. We also specifically investigate ChatGPT's ability to engage in multi-round fixing process (i.e., ChatGPT's dialog ability) of facilitating code generation. By delving into the generated code and examining the experimental results, this work provides valuable insights into the performance of ChatGPT in tackling code generation tasks over the three critical aspects. Overall, our findings uncover potential issues and limitations that arise in the ChatGPT-based code generation and lay the groundwork for improving AI and LLM-based code generation techniques.