Abstract: A method allows an estimator or other party to “Walk the Drawings.” An estimator can open one or more sets of drawings on a mobile device and walk those same electronic drawings as they actually walk or traverse the physical site itself. In other words, as the estimator physically moves across the construction site in the real world, their electronic icon (avatar) moves across the corresponding electronic drawings on their mobile device. Now the estimator can identify present and future such as features that need to be accessible being buried under asphalt. While walking the drawings, the estimator can label any challenges or features by simply clicking their avatar and it will post that geo-stamped location complete with corresponding notes and photos straight to the drawings for later review and analysis.

Abstract: Mechanisms are provided for resolving annotation conflicts in a portion of content. A portion of content is received and annotation operations are performed on it using a plurality of annotators. Each annotator in the plurality of annotators generates a set of annotations for the portion of content, thereby generating a plurality of sets of annotations for the portion of content. The plurality of sets of annotations are analyzed to identify a conflict between a first annotation and a second annotation in the plurality of sets of annotations. The conflict between the first annotation and the second annotation is resolved to generate a resultant set of annotations for the portion of content. The resultant set of annotations is stored in an annotations data structure associated with the portion of content.

Abstract: 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.

Abstract: A mechanism is provided in a data processing system for corpus quality analysis. The mechanism applies at least one filter to a candidate corpus to determine a degree to which the candidate corpus supplements existing corpora for performing a natural language processing (NLP) operation. Responsive to a determination to add the candidate corpus to the existing corpora based on a result of applying the at least one filter, the mechanism adds the candidate corpus to the existing corpora to form modified corpora. The mechanism performs the NLP operation using the modified corpora.

Abstract: Embodiments of the invention provide for the correlation of violating change sets during regression testing of a computer program. A method of the invention includes annotating a test case with a reference to logical operations of different programmatic objects of a computer program. Thereafter, change sets are applied to the program and the test case is executed by a development environment such as a debugger to a point of failure. It is then determined from the annotations change sets related to the logical operations and different ones of the determined change sets are sequentially replaced and the test case repeatedly re-executed. As such, the ones of the replaced change sets resulting in failure from re-execution of the test case are determined to be violating change sets.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Mechanisms are provided to redact sensitive data from a payload. The mechanisms analyze data types in the payload, where the data types correspond to attributes of a person. The mechanisms score the data types as to their sensitivity, which is a measure of a probability that a corresponding data value of the data type, either alone or in combination with other data values, will uniquely identify the person. Each score, or an aggregation of the scores, is compared to a threshold. Responsive to a score, or the aggregation of the scores, being equal to or exceeding the threshold, the mechanisms redact data corresponding to data types whose scores, or the aggregation of scores, are associated. The redacted data is replaced a unique redacted identifier and a data type identifier that identifies at least one data type of the redacted data.

Abstract: A mechanism is provided in a data processing system for corpus quality analysis. The mechanism applies at least one filter to a candidate corpus to determine a degree to which the candidate corpus supplements existing corpora for performing a natural language processing (NLP) operation. Responsive to a determination to add the candidate corpus to the existing corpora based on a result of applying the at least one filter, the mechanism adds the candidate corpus to the existing corpora to form modified corpora. The mechanism performs the NLP operation using the modified corpora.

Abstract: A mechanism is provided in a data processing system for corpus quality analysis. The mechanism applies at least one filter to a candidate corpus to determine a degree to which the candidate corpus supplements existing corpora for performing a natural language processing (NLP) operation. Responsive to a determination to add the candidate corpus to the existing corpora based on a result of applying the at least one filter, the mechanism adds the candidate corpus to the existing corpora to form modified corpora. The mechanism performs the NLP operation using the modified corpora.

Abstract: 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.

Abstract: 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.

Abstract: A first candidate answer to a time-sensitive question received by a question answering (QA) system is identified using a first document. A first temporal confidence subscore of the first candidate answer is then calculated using a first temporal confidence rule. A first general confidence score is then generated based on at least the first temporal confidence subscore. A second candidate answer to the question is identified using a second document. A second temporal confidence subscore of the second candidate answer is then calculated using a second temporal confidence rule. A second general confidence score is then generated based on at least the second temporal confidence subscore. The first general confidence score and the second general confidence score are then compared. Based on this comparison, a final answer is selected from the first candidate answer and the second candidate answer.

Abstract: A first candidate answer to a time-sensitive question received by a question answering (QA) system is identified using a first document. A first temporal confidence subscore of the first candidate answer is then calculated using a first temporal confidence rule. A first general confidence score is then generated based on at least the first temporal confidence subscore. A second candidate answer to the question is identified using a second document. A second temporal confidence subscore of the second candidate answer is then calculated using a second temporal confidence rule. A second general confidence score is then generated based on at least the second temporal confidence subscore. The first general confidence score and the second general confidence score are then compared. Based on this comparison, a final answer is selected from the first candidate answer and the second candidate answer.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments of the invention provide for the correlation of violating change sets during regression testing of a computer program. A method of the invention includes annotating a test case with a reference to logical operations of different programmatic objects of a computer program. Thereafter, change sets are applied to the program and the test case is executed by a development environment such as a debugger to a point of failure. It is then determined from the annotations change sets related to the logical operations and different ones of the determined change sets are sequentially replaced and the test case repeatedly re-executed. As such, the ones of the replaced change sets resulting in failure from re-execution of the test case are determined to be violating change sets.

Abstract: A mechanism is provided in a data processing system for corpus quality analysis. The mechanism applies at least one filter to a candidate corpus to determine a degree to which the candidate corpus supplements existing corpora for performing a natural language processing (NLP) operation. Responsive to a determination to add the candidate corpus to the existing corpora based on a result of applying the at least one filter, the mechanism adds the candidate corpus to the existing corpora to form modified corpora. The mechanism performs the NLP operation using the modified corpora.

Abstract: A first candidate answer to a time-sensitive question received by a question answering (QA) system is identified using a first document. A first temporal confidence subscore of the first candidate answer is then calculated using a first temporal confidence rule. A first general confidence score is then generated based on at least the first temporal confidence subscore. A second candidate answer to the question is identified using a second document. A second temporal confidence subscore of the second candidate answer is then calculated using a second temporal confidence rule. A second general confidence score is then generated based on at least the second temporal confidence subscore. The first general confidence score and the second general confidence score are then compared. Based on this comparison, a final answer is selected from the first candidate answer and the second candidate answer.