Incremental Attribute Evaluation of LR - attributedgrammars Using Space - E cient Data
نویسندگان
چکیده
Incremental attribute evaluation of one-pass attribute grammars (AGs) has not yet been fully investigated. However, considering the lightness of one-pass AGs, combining incremental evaluation and parsing in one-pass AGs may bring about a time-and space-eecient language processor. In this paper, an incremental attribute evaluation method based on LR-attributed grammar, a class of one-pass attribute grammar, is described. First we propose a method of incremental attribute evaluation using a parse tree. Then we present a space-optimized incremental attribute evaluation method using a more compact data structure whose number of nodes is the same as that of tokens. We also describe an optimization to avoid unnecessary re-evaluation, for example, reusing a subtree. Lastly, we describe the implementation of an incremental attribute evaluator generator by augmenting Rie (an attribute evaluator generator based on ECLR-attributed grammar) and show the results of experiments of executing the incremental attribute evaluators generated. As a result of non-optimized incremental attribute evaluation, the execution time for the re-evaluation is from 40% to 80% of the initial evaluation. In the case of optimized incremental attribute evaluation, the time for re-evaluation is from 5% to 15% of the initial evaluation. The results demonstrate the eeciency of incremental re-evaluation of one-pass attribute grammars.
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