"Language-model surprisal, already associated with cognitive processing costs in natural-language reading, also predicts aspects of human code-comprehension difficulty."
The researchers extended an established psycholinguistic framework to programming-language comprehension through a controlled experiment. Higher code surprisal significantly increased the time needed to complete comprehension tasks, demonstrating a measurable connection between statistical language modeling and human interpretation of software.
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