Navigating LLM-Generated Code: An Analysis of Library Recommendations and Usability Challenges

TLDR: An empirical study found that LLMs generally recommend robust, popular, and permissively licensed third-party Python libraries. However, a small percentage of these recommendations don’t work out of the box due to naming aliases or module-level imports, and LLMs often fail to provide installation guidance, placing the burden of dependency resolution on developers. The study highlights the need for better contextual support in LLM-generated code for practical usability and security.

Large Language Models (LLMs) are rapidly becoming indispensable tools for software developers, assisting with everything from writing new code to debugging existing programs. As their use becomes more widespread, a critical question arises: how reliable are the software libraries these AI models recommend and include in the code they generate? A recent empirical study delves into this very question, examining the robustness of LLM-generated library imports and uncovering valuable insights for both developers and researchers.

The study, titled “How Robust are LLM-Generated Library Imports? An Empirical Study using Stack Overflow,” was conducted by Jasmine Latendresse, SayedHassan Khatoonabadi, and Emad Shihab. Their research aimed to understand the types of libraries LLMs recommend, their characteristics, and how readily these recommendations can be used “out of the box.”

How the Study Was Conducted

To assess LLM behavior in a realistic setting, the researchers prompted six state-of-the-art LLMs (including proprietary models like GPT-4 Turbo and open-source ones like Llama 3.1 and DeepSeek V3) with 112 real-world Python programming questions sourced from Stack Overflow. These questions reflect common scenarios where developers seek assistance with coding tasks, often involving the use or selection of software libraries. The generated code was then analyzed to identify the imported libraries and classify them as either standard (built-in Python), third-party (installable via PyPI), or unknown.

Key Findings: What Libraries Do LLMs Prefer?

The study revealed a consistent pattern across all evaluated LLMs: they predominantly favor third-party libraries over standard ones. Out of 87 distinct libraries identified across all generated code, 54% were third-party, 41% were standard, and a small but significant 4.6% were classified as “unknown.” This indicates a strong reliance on external packages rather than Python’s built-in functionalities.

When it comes to the characteristics of these recommended third-party libraries, the findings are largely positive. LLMs tend to suggest libraries that are mature, popular, and generally safe for production environments. These libraries typically have a high number of GitHub stars and forks, indicating widespread adoption and community trust. They also tend to be well-maintained, with low numbers of transitive dependencies (meaning they don’t bring in a lot of other hidden libraries) and consistent update frequencies. The median age of recommended libraries was over seven years, suggesting a preference for stable, long-standing options.

The Usability Gap: When Recommendations Don’t Work Out of the Box

Despite the overall robustness, the study identified a crucial usability gap: some recommended libraries do not work immediately. The 4.6% of “unknown” libraries primarily stemmed from two issues:

• Aliasing: This is the most common problem. LLMs often use an alias (a different name) for a library in the import statement than its actual installable package name. For example, cv2 is an alias for opencv-python, and yaml is an alias for PyYAML. While the import statement itself is technically correct, a developer trying to install cv2 directly would fail.
• Module-level Imports: In some rare cases, the LLM might suggest importing a submodule directly without explicitly referencing its parent library. For instance, from client import EmailageClient where client is a submodule of the emailage library.

A significant concern highlighted by the study is that LLMs rarely provide installation guidance for these aliased or module-level imports. Only 5 out of 24 alias cases included instructions like pip install opencv-python. This places the burden on the developer to manually figure out the correct package name, which can be particularly challenging for less experienced users.

Also Read:

• The Hidden Flaw: How Large Language Models Handle Bad Code Instructions
• Navigating AI Code Generation: Strategies for Productive Human-LLM Interaction

Implications for Developers and Researchers

The research offers several practical considerations:

• Licensing Awareness: While most recommended libraries are under permissive licenses (like MIT or Apache-2.0), a few instances of copyleft licenses were observed. LLMs typically don’t disclose license information, so developers must manually verify licenses to ensure legal compliance, especially in commercial projects.
• Users as Active Evaluators: LLM outputs should be treated as a starting point, not a final solution. Developers need to actively validate and resolve dependencies, especially when installation instructions are missing.
• Dependency Management: The LLMs’ preference for third-party libraries, while often good quality, can increase codebase complexity and lead to “dependency hell” over time. Future tools could integrate maintenance indicators to help developers make informed decisions.
• Security Implications: Although no “hallucinated” (non-existent) libraries were found in this study, the presence of aliased or placeholder imports could be exploited by malicious actors through “dependency confusion” attacks if users install unverified packages. This underscores the need for built-in mechanisms to verify the validity and safety of recommended libraries.

In conclusion, while LLMs demonstrate a strong ability to recommend valid and appropriate software libraries, their practical usability is sometimes hindered by a lack of contextual support for dependency resolution. The full research paper can be accessed here. Future advancements in LLM tooling should focus on providing more transparent and execution-aware outputs, including automatic installation commands and license information, to truly streamline developer workflows.