Parsing Formal Languages using Natural Language Parsing Techniques

Program analysis tools used in software maintenance must be robust and ought to be accurate. Many data-driven parsing ap-proaches developed for natural languages are robust and have quite high accuracy when applied to parsing of software. We show this for the programming languages Java, C/C++, and Python. Further studies indicate that post-processing can almost completely remove the remaining errors. Finally, the training data for instantiating the generic data-driven parser can be gen-erated automatically for formal languages, as opposed to the manually development of treebanks for natural languages. Hence, our approach could improve the robust-ness of software maintenance tools, proba-bly without showing a significant negative effect on their accuracy.