International Business Machines Corporation

Pamela D Andrejko

Andrew Ronald Freed

Richard A Salmon

Cynthia M Murch

Charles S Skinner

Jan M Nordland

Humberto R Rivero

Robert L Nielsen

A primary ingestion pipeline configured for use in natural language processing includes annotators configured for annotating documents. The annotators and documents to be annotated are evaluated. Based on the evaluations, an ingestion risk score is generated for each document. Each ingestion risk score represents a likelihood that an associated document will not successfully be annotated by the annotators. Each ingestion risk score is compared to a set of risk criteria. Based on the comparisons, a determination is made that each document of a first set of documents satisfies the set of risk criteria. A further determination is made, based on the comparisons, that each document of a second set of documents does not satisfy the set of risk criteria. In response to these determinations, the first set of documents is entered into the primary ingestion pipeline and the second set of documents is provided special handling.

Ingesting documents using multiple ingestion pipelines

Mechanisms are provided for evaluating test cases for testing a software product based on a requirements change. The mechanisms analyze a test case corpus to identify a plurality of first relationships between elements of test cases in the test case corpus and generate a test case relationship model based on the identified plurality of first relationships. The mechanisms receive a proposed requirements change to change one or more requirements of the software product and then perform a search of the test case relationship model to identify test case relationships corresponding to the proposed requirements change. The mechanisms identify a subset of test cases affected by the proposed requirements change and generate an output specifying the identified subset of test cases.

Proactive cognitive analysis for inferring test case dependencies

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.

Generating natural language text sentences as test cases for NLP annotators with combinatorial test design

An approach is provided in which an information handling system analyzes correction information corresponding to an answer generated by a question answer system. The correction information includes a correction to the answer and captured variable information utilized by the question answer system to generate the answer. The information handling system selects input variables based upon the correction analysis and generates a test case that includes the selected input variables and the variable information. In turn, the information handling system tests the question answer system using the generated test case.

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Cary, NC, United States of America

Pamela D Andrejko