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

What is it about?

The paper addresses a common issue in C programming—typos or keyboard errors (such as stuck keys, deletions, mis-typings, or swapped characters) in keywords that typical compilers or IDEs can't handle. To mitigate this, an innovative fuzzy-automaton model for approximate string matching during lexical analysis is proposed. Fuzziness to lexemes is introduced, allowing the lexical analyzer to recognize and correct “fuzzy tokens” (i.e., near-misses of valid keywords). The algorithms and pseudo-code to compute membership degrees of lexemes, being crisp (exact) or fuzzy, are proposed. The fuzzy system is then trained using a neural network to assess its accuracy

Featured Image

Why is it important?

To automatically detect and correct minor keyword typos via fuzzy matching and neural validation.