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Enhancing Readability and Machine Learning through Coding Standards and Metrics

This paper explores the intersection of readability metrics, machine learning, and coding standards, drawing on influential works from various authors. It highlights the evolution of readability assessments, including the Flesch and Gunning scores, and their applications in natural language processing. By examining foundational research alongside contemporary practices, the paper emphasizes the importance of standardization in coding and readability metrics. The contribution of various scholars, such as Sutter and Alexandrescu, is acknowledged as essential to the development of effective machine learning strategies focused on text comprehensibility.

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Enhancing Readability and Machine Learning through Coding Standards and Metrics

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  1. H. Sutter and A. Alexandrescu 2004 L. W. Cannon, Et Al 1990 T. Copeland. 2005 T. M. Khoshgoftaar, Et Al 1996 T. J. Cheatham Et Al 1995 P. Knab, Et Al 2006 Standard Checkers S. Ambler 1997 Coding Standards T. L. Graves, Et Al 2000 Machine Learning This Paper 2008 A. E. Hatzimanikatis, Et Al 1997 G. H. McLaughlin 1968 Hypertext Readability Metrics J. P. Kinciad and E. A. Smith 1970 Treemaps Natural Language P. Anderson and T. Teitelbaum 2001 Math R. F. Flesch 1948 S. MacHae, Et Al 1997 R. Gunning 1952

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