LLMs match or exceed state-of-the-art traditional methods for stabilizing numerical expressions in scientific software, succeeding on 97.9% of expressions where baselines fail to improve accuracy, but struggle with control flow and high-precision literals.
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TARIPlay detects and tracks viable interactive areas in AR playback videos using stability and visibility criteria, achieving 55.8% branch coverage on AR-related code versus 41.98% for Monkey across four apps and nine videos.
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Assessing Large Language Models for Stabilizing Numerical Expressions in Scientific Software
LLMs match or exceed state-of-the-art traditional methods for stabilizing numerical expressions in scientific software, succeeding on 97.9% of expressions where baselines fail to improve accuracy, but struggle with control flow and high-precision literals.
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TARIPlay: A Test Framework for AR Applications based on Interactive Area Tracking in Playback Videos
TARIPlay detects and tracks viable interactive areas in AR playback videos using stability and visibility criteria, achieving 55.8% branch coverage on AR-related code versus 41.98% for Monkey across four apps and nine videos.