1 st Doctoral Symposium of the International Conference on Software Language

The use of unconstrained context-free grammars for generalized parsing techniques has several advantages over traditional grammar classes, but comes with the danger of undiscovered ambiguities. The ambiguity problem for these grammars is undecidable in the general case, but this does not have to be a problem in practice. Our goal is to find ambiguity detection techniques that have sufficient precision and performance to make them suitable for practical use on realistic grammars. We give a short overview of related work, and propose new directions for improvement. 1 Problem Description and Motivation Generalized parsing techniques allow the use of the entire class of context-free grammars (CFGs) for the specification of the syntax of programming languages. This has several advantages. First, it allows for modular syntax definitions, which simplifies grammar development and enables reuse. Second, it grants total freedom in structuring a grammar to best fit its intended use. Grammars do not have to be squeezed into LL, LALR or LR(k) form for instance. Unfortunately, using unconstrained context-free grammars comes with the danger of ambiguities. A grammar is ambiguous if one or more sentences in its language have multiple parse trees. The semantics of a sentence is usually based upon the structure of its parse tree, so an ambiguous sentence can have multiple meanings. This often indicates a grammar bug which should be avoided. However, in some cases a grammar is intended to contain some degree of ambiguity. For instance in reverse engineering, where certain legacy languages can only be disambiguated with type checking after parsing. In both cases it is important to know the sources of ambiguity in the developed grammar, so they can be resolved or verified. Unfortunately, detecting the (un)ambiguity of a grammar is undecidable in the general case [7, 10, 9]. However, this does not necessarily have to be a problem in practice. Several Ambiguity Detection Methods (ADMs) exist that approach the problem from different angles, all with their own strengths and weaknesses. Because of the undecidability of the problem there is a general tradeoff between precision and performance/termination. The challenge for all ADMs is to give the most precise and understandable answer in the time available. The current state