A code inspection of 'serious' LLM-generated code (ea.rna.nl)

Recent insights into the limitations of language model (LLM)-generated code have emerged from a detailed inspection, revealing the challenges associated with maintaining code quality in AI-assisted programming. This analysis, conducted using the Institute for Software Quality's (IfSQ) Level 2 audit, highlighted that while LLMs can produce functional code, they often yield complex, bloated structures that can be difficult for human engineers to maintain. The overall score from the audit was categorized as "FF," indicating significant issues in areas such as structured programming and single points of maintenance. This audit underscores the disparity between the capabilities of LLM-generated outputs and the rigorous standards expected in software engineering. The findings classify LLM-generated code into three categories: AI-Slop (incorrect results), AI-Sludge (correct but unrefined and convoluted), and the desired AI-Vakwerk (solid, quality code). Despite the potential for LLMs to produce accurate results, the audit's emphasis on maintainability underscores the important consideration for developers: AI-generated code may require substantial human intervention to ensure it aligns with maintainable standards. As LLMs continue to evolve, these insights serve as a pivotal reminder for the AI/ML community to prioritize code quality and clarity to effectively harness the power of generative AI in real-world applications.