# A Taxonomy of Inefficiencies in LLM-Generated Python Code

arXiv: https://arxiv.org/abs/2503.06327 · HTML: https://mlsanity.com/paper/2503.06327/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2025.

## Built on by (most cited first, 10 of 10)

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- [Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code](https://mlsanity.com/paper/2605.05267/) (2026)
- [SpecDB: LLM-Generated Customized Databases via Feature-Oriented Decomposition](https://mlsanity.com/paper/2605.31097/) (2026)
- [ReCatcher: Towards LLMs Regression Testing for Code Generation](https://mlsanity.com/paper/2507.19390/) (2025)
- [TRIM: Reducing AI-Generated CodeSlop via Agent Trajectory Minimization](https://mlsanity.com/paper/2607.18161/) (2026)
- [Not All Correct Fixes Run Alike: An Empirical Study of Patch Performance in Repository-Level Issue Resolution](https://mlsanity.com/paper/2610.13563/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are widely adopted for automated code generation with promising results. Although prior research has assessed LLM-generated code and identified various quality issues -- such as redundancy, poor maintainability, and sub-optimal performance a systematic understanding and categorization of these inefficiencies remain unexplored. Without such knowledge, practitioners struggle to optimize LLM-generated code for real-world applications, limiting its adoption. This study can also guide improving code LLMs, enhancing the quality and efficiency of code generation. Therefore, in this study, we empirically investigate inefficiencies in LLM-generated code by state-of-the-art models, i.e., CodeLlama, DeepSeek-Coder, and CodeGemma. To do so, we analyze 492 generated code snippets in the HumanEval++ dataset. We then construct a taxonomy of inefficiencies in LLM-generated code that includes 5 categories General Logic, Performance, Readability, Maintainability, and Errors) and 19 subcategories of inefficiencies. We then validate the proposed taxonomy through an online survey with 58 LLM practitioners and researchers. Our study indicates that logic and performance-related inefficiencies are the most popular, relevant, and frequently co-occur and impact overall code quality inefficiency. Our taxonomy provides a structured basis for evaluating the quality LLM-generated code and guiding future research to improve code generation efficiency.