On-the-Fly Syntax Highlighting: Generalisation and Speed-ups
CoRR(2024)
摘要
On-the-fly syntax highlighting is the task of rapidly associating visual
secondary notation values with each character of a language derivation.
Research in this domain is driven by the prevalence of online software
development tools, which frequently display source code on screen and heavily
rely on syntax highlighting mechanisms. In this context, three contrasting
demands confront resolvers in this space: speed, accuracy, and development
costs. Speed constraints are essential to ensure tool usability, manifesting as
responsiveness for end users accessing online source code and minimising system
overhead. Simultaneously, achieving precise highlighting is critical for
enhancing code comprehensibility. Nevertheless, obtaining accurate results
necessitates the capacity to perform grammatical analysis on the code under
consideration, even in cases of varying grammatical correctness. Furthermore,
addressing the development costs of such resolvers is imperative, given the
multitude of programming language versions. The current state-of-the-art
approach in this field leverages the original lexer and parser of programming
languages to create syntax highlighting oracles, subsequently used for training
base Recurrent Neural Network models. As the question of the generalisation of
such a solution persists, this paper addresses this aspect by extending the
original work to three additional mainstream programming languages and
conducting a comprehensive review of the outcomes. Moreover, the original
limitations in evaluation performance and training costs are mitigated through
the introduction of a novel Convolutional based Neural Network model. This
study examines the performance gains of running models on GPUs, finding that
the new CNN implementation is much faster than previous methods while
maintaining high accuracy.
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