# Large Language Models and Simple, Stupid Bugs

arXiv: https://arxiv.org/abs/2303.11455 · HTML: https://mlsanity.com/paper/2303.11455/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 64 references that are in the map.
- First posted on arXiv 20 Mar 2023.

## Builds on (most cited first, 30 of 39)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [InCoder: A Generative Model for Code Infilling and Synthesis](https://mlsanity.com/paper/2204.05999/) (2022)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [Unified Pre-training for Program Understanding and Generation](https://mlsanity.com/paper/2103.06333/) (2021)
- [A Systematic Evaluation of Large Language Models of Code](https://mlsanity.com/paper/2202.13169/) (2022)
- [Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2006.03511/) (2020)
- [Do Users Write More Insecure Code with AI Assistants?](https://mlsanity.com/paper/2211.03622/) (2022)
- [How Context Affects Language Models' Factual Predictions](https://mlsanity.com/paper/2005.04611/) (2020)
- [SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair](https://mlsanity.com/paper/1901.01808/) (2018)
- [Few-shot training LLMs for project-specific code-summarization](https://mlsanity.com/paper/2207.04237/) (2022)
- [The Adverse Effects of Code Duplication in Machine Learning Models of Code](https://mlsanity.com/paper/1812.06469/) (2018)
- [Examining Zero-Shot Vulnerability Repair with Large Language Models](https://mlsanity.com/paper/2112.02125/) (2021)
- [Jigsaw: Large Language Models meet Program Synthesis](https://mlsanity.com/paper/2112.02969/) (2021)
- [Evaluating the Text-to-SQL Capabilities of Large Language Models](https://mlsanity.com/paper/2204.00498/) (2022)
- [A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Level](https://mlsanity.com/paper/2112.15594/) (2021)
- [Is GitHub's Copilot as Bad as Humans at Introducing Vulnerabilities in Code?](https://mlsanity.com/paper/2204.04741/) (2022)
- [Applying CodeBERT for Automated Program Repair of Java Simple Bugs](https://mlsanity.com/paper/2103.11626/) (2021)
- [No More Fine-Tuning? An Experimental Evaluation of Prompt Tuning in Code Intelligence](https://mlsanity.com/paper/2207.11680/) (2022)
- [What is it like to program with artificial intelligence?](https://mlsanity.com/paper/2208.06213/) (2022)
- [Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models](https://mlsanity.com/paper/2301.03797/) (2023)
- [How Often Do Single-Statement Bugs Occur? The ManySStuBs4J Dataset](https://mlsanity.com/paper/1905.13334/) (2019)
- [CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing](https://mlsanity.com/paper/2104.02443/) (2021)
- [Automatic Program Repair with OpenAI's Codex: Evaluating QuixBugs](https://mlsanity.com/paper/2111.03922/) (2021)

## Built on by (most cited first, 30 of 45)

- [Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code](https://mlsanity.com/paper/2404.00971/) (2024)
- [Can ChatGPT replace StackOverflow? A Study on Robustness and Reliability of Large Language Model Code Generation](https://mlsanity.com/paper/2308.10335/) (2023)
- [Bugs in Large Language Models Generated Code: An Empirical Study](https://mlsanity.com/paper/2403.08937/) (2024)
- [Calibration and Correctness of Language Models for Code](https://mlsanity.com/paper/2402.02047/) (2024)
- [A Performance Study of LLM-Generated Code on Leetcode](https://mlsanity.com/paper/2407.21579/) (2024)
- [Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices](https://mlsanity.com/paper/2403.12503/) (2024)
- [Automatic Programming: Large Language Models and Beyond](https://mlsanity.com/paper/2405.02213/) (2024)
- [AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement](https://mlsanity.com/paper/2412.06176/) (2024)
- [CodeHalu: Investigating Code Hallucinations in LLMs via Execution-based Verification](https://mlsanity.com/paper/2405.00253/) (2024)
- [Prompting Techniques for Secure Code Generation: A Systematic Investigation](https://mlsanity.com/paper/2407.07064/) (2024)
- [Correctness Assessment of Code Generated by Large Language Models Using Internal Representations](https://mlsanity.com/paper/2501.12934/) (2025)
- [LLM-Powered Code Vulnerability Repair with Reinforcement Learning and Semantic Reward](https://mlsanity.com/paper/2401.03374/) (2024)
- [Calibration of Large Language Models on Code Summarization](https://mlsanity.com/paper/2404.19318/) (2024)
- [Studying LLM Performance on Closed- and Open-source Data](https://mlsanity.com/paper/2402.15100/) (2024)
- [Hallucination by Code Generation LLMs: Taxonomy, Benchmarks, Mitigation, and Challenges](https://mlsanity.com/paper/2504.20799/) (2025)
- [Goedel-Code-Prover: Hierarchical Proof Search for Open State-of-the-Art Code Verification](https://mlsanity.com/paper/2603.19329/) (2026)
- [Code Security Vulnerability Repair Using Reinforcement Learning with Large Language Models](https://mlsanity.com/paper/2401.07031/) (2024)
- [A Deep Dive Into Large Language Model Code Generation Mistakes: What and Why?](https://mlsanity.com/paper/2411.01414/) (2024)
- [Risk Assessment Framework for Code LLMs via Leveraging Internal States](https://mlsanity.com/paper/2504.14640/) (2025)
- [A Language Model of Java Methods with Train/Test Deduplication](https://mlsanity.com/paper/2305.08286/) (2023)
- [Impeding LLM-assisted Cheating in Introductory Programming Assignments via Adversarial Perturbation](https://mlsanity.com/paper/2410.09318/) (2024)
- [AIGCodeSet: A New Annotated Dataset for AI Generated Code Detection](https://mlsanity.com/paper/2412.16594/) (2024)
- [Enhancing Source Code Security with LLMs: Demystifying The Challenges and Generating Reliable Repairs](https://mlsanity.com/paper/2409.00571/) (2024)
- [What Breaks When LLMs Code? Characterizing Operational Safety Failures of Agentic Code Assistants](https://mlsanity.com/paper/2605.30777/) (2026)
- [Harnessing the Power of LLMs: Automating Unit Test Generation for High-Performance Computing](https://mlsanity.com/paper/2407.05202/) (2024)
- [Where's the Bug? Attention Probing for Scalable Fault Localization](https://mlsanity.com/paper/2502.13966/) (2025)
- [Localized Calibrated Uncertainty in Code Language Models](https://mlsanity.com/paper/2512.24560/) (2025)
- [N-Version Assessment and Enhancement of Generative AI](https://mlsanity.com/paper/2409.14071/) (2024)
- [MigGPT: Harnessing Large Language Models for Automated Migration of Out-of-Tree Linux Kernel Patches Across Versions](https://mlsanity.com/paper/2504.09474/) (2025)
- [MultiAIGCD: A Comprehensive dataset for AI Generated Code Detection Covering Multiple Languages, Models,Prompts, and Scenarios](https://mlsanity.com/paper/2507.21693/) (2025)

## Abstract (arXiv)

With the advent of powerful neural language models, AI-based systems to assist developers in coding tasks are becoming widely available; Copilot is one such system. Copilot uses Codex, a large language model (LLM), to complete code conditioned on a preceding "prompt". Codex, however, is trained on public GitHub repositories, viz., on code that may include bugs and vulnerabilities. Previous studies [1], [2] show Codex reproduces vulnerabilities seen in training. In this study, we examine how prone Codex is to generate an interesting bug category, single statement bugs, commonly referred to as simple, stupid bugs or SStuBs in the MSR community. We find that Codex and similar LLMs do help avoid some SStuBs, but do produce known, verbatim SStuBs as much as 2x as likely than known, verbatim correct code. We explore the consequences of the Codex generated SStuBs and propose avoidance strategies that suggest the possibility of reducing the production of known, verbatim SStubs, and increase the possibility of producing known, verbatim fixes.