Communications of the ACM - Special issue on parallelism
The automatic generation of Fast Lexical Analysers
Software—Practice & Experience
Mkscan—an interactive scanner generator
Software—Practice & Experience
LexAGen: an interactive incremental scanner generator
Software—Practice & Experience
Efficient generation of lexical analyzers
Software—Practice & Experience
Incremental Scanning and Parsing with Galaxy
IEEE Transactions on Software Engineering
Conflict detection and resolution in a lexical analyzer generator
Information Processing Letters
Incremental generation of lexical scanners
ACM Transactions on Programming Languages and Systems (TOPLAS)
ALADIN: a scanner generator for incremental programming environments
Software—Practice & Experience
Efficient string matching: an aid to bibliographic search
Communications of the ACM
Alex—A Simple and Efficient Scanner Generator
ACM SIGPLAN Notices
On the applicability of the longest-match rule in lexical analysis
Computer Languages, Systems and Structures
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Lexical analyzers partition input characters into tokens. When ambiguities arise during lexical analysis, the longest-match rule is generally adopted to resolve the ambiguities. The longest-match rule causes the look-ahead problem in traditional lexical analyzers, which are based on Moore machines. In Moore machines, output tokens are associated with states of the automata. By contrast, because Mealy machines associate output tokens with state transitions, the look-ahead behaviors can be encoded in their state transition tables. Therefore, we believe that lexical analyzers should be based on Mealy machines, rather than Moore machines, in order to solve the look-ahead problem. We propose techniques to construct Mealy machines from regular expressions and to perform sequential and data-parallel lexical analysis with these Mealy machines.