Abstract: Floating-point errors represent an inherent challenge in computer science,particularly in precision-sensitive domains such as scientific computing and financial analysis where such errors may lead to severe consequences.This study conducts a systematic analysis of floating-point error characteristics using four representative test suites comprising 109 benchmark programs,with particular focus on investigating the impact of floating-point precision,exponents and expression types to identify potential error regularities.Experimental results demonstrate statistically significant patterns in error distributions:1)98.46% of floating-point expressions exhibit identical error distribution trends between single-precision(FP32) and double-precision(FP64) formats;2)For dual-parameter expressions with multiplicative/divisive relationships,86.67% of significant errors occur when the exponents of both parameters demonstrate linear dependence;3)90.91% of rational expressions manifest maximum errors within critical neighborhoods where denominators approach zero.These empirical regularities in floating-point error behavior not only advance fundamental understanding of numerical inaccuracies but also provide actionable insights for enhancing error detection tools,optimizing numerical algorithms,and formulating rigorous error control strategies.

Key words: Floating-point errors, Floating-point expressions, Error characteristics, Floating-point arithmetic, Error detection