Generalized parsers for visual languages result to be more complex than the corresponding text parsers: their applications to ambiguous inputs with visual tokens scattered and/or connected on a 2d or 3d space are very...
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ISBN:
(纸本)9781728108100
Generalized parsers for visual languages result to be more complex than the corresponding text parsers: their applications to ambiguous inputs with visual tokens scattered and/or connected on a 2d or 3d space are very difficult to trace without the support of proper visualization tools. In this work, we give a brief description of generalized parsers for visual structures and illustrate a tool for the visualization of its execution. To do this, we provide examples based on a Visual Generalized LR (VGLR) parser applied to flowcharts.
In this paper, we present ParVis, an interactive visual system for the animated visualization of logged parser trace executions. The system allows a parser implementer to create a visualizer for generated parsers by s...
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In this paper, we present ParVis, an interactive visual system for the animated visualization of logged parser trace executions. The system allows a parser implementer to create a visualizer for generated parsers by simply defining a JavaScript module that maps each logged parser instruction into a set of events driving the visual system interface. The result is a set of interacting graphical/text windows that allows users to explore logged parser executions and helps them to have a complete understanding of how the parser behaves during its execution on a given input. We used our system to visualize the behavior of textual as well as visual parsers and describe here two of these uses. Moreover, in order to validate the efficacy of our system, we ran a user experiment where students analyzed a CUP-generated parser both with ParVis, instantiated to LALR parsers, and the standard CUP debug facilities. The results show that students can indeed analyze parser behavior and find mistakes in parser specifications more easily and quickly using ParVis. In particular, in some parser design tasks, using ParVis participants achieved a higher success rate of 50% in 42% less time with respect to the baseline system.
In this paper, we present PARVIS, an interactive visual system for the animated visualization of logged parser trace executions. The system allows a parser implementer to create a visualizer for generated parsers by s...
详细信息
ISBN:
(纸本)9781450375351
In this paper, we present PARVIS, an interactive visual system for the animated visualization of logged parser trace executions. The system allows a parser implementer to create a visualizer for generated parsers by simply defining a JavaScript module that maps each logged parser instruction into a set of events driving the visual system interface. The result is a set of interacting graphical/text windows that allows users to explore logged parser executions and helps them to have a complete understanding of how the parser behaves during its execution on a given input. We used our system to visualize the behavior of textual as well as visual parsers and describe here its use with the well known CUP parser generator. Preliminary tests with users have provided good feedback on its use.
This work is focused on syntax error recovery, a complex aspect of syntax analysis. We have enhanced a parser visualization tool called VAST to produce educational visualizations of this topic. Compiler visualization ...
详细信息
ISBN:
(纸本)9788493981471
This work is focused on syntax error recovery, a complex aspect of syntax analysis. We have enhanced a parser visualization tool called VAST to produce educational visualizations of this topic. Compiler visualization is not a new research field, but the authors have not found any visualization tool dealing with this topic. We have evaluated the enhanced version of VAST and its visualizations about syntax error recovery strategies. We have detected that students who used the enhanced tool improved their performance developing syntax specifications with error recovery. Consequently, these students significantly improved their learning outcomes regarding error recovery exercises.
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