  With the presence of online collaborative tools for software developers,
source code is shared and consulted frequently, from code viewers to merge
requests and code snippets. Typically, code highlighting quality in such
scenarios is sacrificed in favor of system responsiveness. In these on-the-fly
settings, performing a formal grammatical analysis of the source code is not
only expensive, but also intractable for the many times the input is an invalid
derivation of the language. Indeed, current popular highlighters heavily rely
on a system of regular expressions, typically far from the specification of the
language's lexer. Due to their complexity, regular expressions need to be
periodically updated as more feedback is collected from the users and their
design unwelcome the detection of more complex language formations. This paper
delivers a deep learning-based approach suitable for on-the-fly grammatical
code highlighting of correct and incorrect language derivations, such as code
viewers and snippets. It focuses on alleviating the burden on the developers,
who can reuse the language's parsing strategy to produce the desired
highlighting specification. Moreover, this approach is compared to nowadays
online syntax highlighting tools and formal methods in terms of accuracy and
execution time, across different levels of grammatical coverage, for three
mainstream programming languages. The results obtained show how the proposed
approach can consistently achieve near-perfect accuracy in its predictions,
thereby outperforming regular expression-based strategies.
