GENERATING NATURAL LANGUAGE TEXT SENTENCES AS TEST CASES FOR NLP ANNOTATORS WITH COMBINATORIAL TEST DESIGN

Test cases for a text annotator are generated by determining types of inputs to the annotator and analyzing language structures in a corpus to identify sentence types and grammar constructs. An input type can correspond to multiple grammar constructs. Test cases are generated by performing grammar tree transformations on selected fragments from the corpus based on the sentence types and the grammar constructs. Additional test cases are generated by replacing starting phrases in selected fragments with substitute phrases from dictionaries associated with the input types (a dictionary can include a false synonym for an input type for purposes of negative testing). The two generating approaches can be combined, i.e., performing one or more successive (different) grammar tree transformations to yield a sentence which is then subjected to phrase substitution.