Statistical analysis of lexemes generated in ‘C’ programming using fuzzy automation

Subtitle: An Empirical approach to NextGen Software

Article type: Research Article

Authors: Kaur, Ranjeet^{a; b; *} | Tripathi, Alka^a

Affiliations: [a] Department of Mathematics, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India | [b] ABES Engineering College, Ghaziabad, Uttar Pradesh, India

Correspondence: [*] Corresponding author. Ranjeet Kaur, Email: [email protected].

Abstract: The present work is an effort to support the typographical errors of keywords that are not supported by existing compilers and integrated development environment(IDE) in ’C’ language. The fuzzy automata modelling approximate string matching is proposed for error handling during lexical analysis. By introducing fuzziness to lexemes the typographical errors can be rectified at the time of compilation and flexibility of lexical analyser can be greatly improved. The recognition of fuzzy tokens during lexical analysis is described in order to correct errors caused by sticking key, deletion, typing and swapping key in keywords during C programming. Algorithms and pseudo code are being developed to measure the degree of membership of crisp and fuzzy lexemes. Accuracy is tested and examined once the fuzzy lexemes are trained using a neural network. The proposed method is an add on feature that can be incorporated in existing compilers and IDEs to increase their flexibility.

Keywords: Fuzzy lexemes, fuzzy automata, error handling, approximate string matching, fuzzy lexical analysis

DOI: 10.3233/JIFS-223021

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2537-2546, 2024