arXiv:2605.22976v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly integrated into software systems for diverse purposes, due to their versatility, flexibility, and ability to simulate human reasoning to some extent. However, poor integration of LLM inference in source code can undermine software system quality. Therefore, inadequate LLM integration coding practices must be documented to help developers mitigate such issues. Following our earlier work on LLM code smells, this paper consolidates and refines the concept by presenting a self-contained taxonomy and a c
As LLMs become ubiquitous in software development, the community is now grappling with best practices and identifying pitfalls, leading to standardization efforts like taxonomies for 'code smells'.
This development indicates a maturing understanding of LLM integration into software, crucial for ensuring reliability, maintainability, and security of AI-powered systems.
The formal cataloging of 'LLM code smells' provides developers with a structured approach to identify and mitigate issues related to LLM integration, improving software quality.
- · Software developers
- · Organizations using LLMs in products
- · AI safety researchers
- · Code quality tooling vendors
- · Developers ignoring LLM integration best practices
- · Systems with poor LLM architectural design
Improved quality and reliability of software systems integrating LLMs.
Development of automated tools for detecting and fixing LLM-specific code smells, akin to traditional static analysis.
Enhanced trust in AI-powered applications due to fewer integration flaws and higher overall system integrity.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI