arXiv:2606.31308v1 Announce Type: new Abstract: This paper investigates the capability of Large Language Models (LLMs) to detect and classify floating-point errors statically in software code. We introduce InterFLOPBench, a benchmark of 90 C kernels with 1 130 test samples designed to evaluate LLMs across six categories of floating-point error: cancellation, comparison, division by zero, overflow, underflow and NaN, compared across 14 LLMs. The evaluation framework treats floating-point error detection as a multi-label classification problem and employs the F1-score metric to measure performan
As LLMs become more sophisticated and integrated into complex software development, their ability to handle subtle but critical programming errors like floating-point issues becomes a key focus for reliability and safety.
This research highlights the growing role of AI in automating highly technical and error-prone aspects of software engineering, potentially enhancing code quality and reducing development costs across various industries.
The explicit benchmarking of LLMs on specific and critical code error types demonstrates a measurable advancement in AI's capacity for code analysis and bug detection, moving beyond general code generation.
- · Software developers
- · AI model developers
- · SaaS platforms offering AI code analysis
- · Industries reliant on precise calculations
- · Manual code reviewers
- · Traditional bug detection software
Improved software reliability and efficiency through AI-powered error detection.
Increased adoption of AI tools in software development pipelines, changing workflow dynamics for engineers.
Potential for AI to eventually autonomously refactor and self-correct complex codebases, accelerating innovation cycles in software.
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Read at arXiv cs.AI