Abstract
Ambiguous Context Free Grammars (CFGs) are problematic for programming languages, as they allow inputs to be parsed in more than one way. In this paper, we introduce a simple non-deterministic search-based approach to ambiguity detection which non-exhaustively explores a grammar in breadth for ambiguity. We also introduce two new techniques for generating random grammars – Boltzmann sampling and grammar mutation – allowing us to test ambiguity detection tools on much larger corpuses than previously possible. Our experiments show that our breadth-based approach to ambiguity detection performs as well as, and generally better, than extant tools.
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Vasudevan, N., Tratt, L. (2013). Detecting Ambiguity in Programming Language Grammars. In: Erwig, M., Paige, R.F., Van Wyk, E. (eds) Software Language Engineering. SLE 2013. Lecture Notes in Computer Science, vol 8225. Springer, Cham. https://doi.org/10.1007/978-3-319-02654-1_9
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DOI: https://doi.org/10.1007/978-3-319-02654-1_9
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