Abstract: A computer-implemented method, system and computer program product for performing user acceptance testing. A virtual environment for virtual sponsor users to interact with a feature or a function of a software product is created. Such virtual sponsor users are virtual representations of actual or real sponsor users. Each of these virtual sponsor users are then assigned a persona type profile of a target audience. Such persona type profiles include the character traits of a user, such as a user or member of the target audience. Interactions of the virtual sponsor users with the feature or the function of the software product are then simulated and tracked. The rate of acceptance of the feature or the function of the software product is then determined based on such tracked interactions. In this manner, user acceptance testing is performed in a swiftly manner that is cost effective.

Abstract: Techniques for data curation are provided. A data set is received for ingestion into a question answering system, where the data set includes a first question and a first answer. Relevance of the first question is validated by comparing the first question to a first question cluster in the question answering system, and it is determined that the first answer satisfies predefined security criteria. The first data set is evaluated to identify a set of references, and a generalized data set is generated by replacing each respective reference of the set of references with a corresponding entity identifier. The first generalized data set is then ingested into the question answering system.

Abstract: Techniques for data curation are provided. A data set is received for ingestion into a question answering system, where the data set includes a first question and a first answer. Relevance of the first question is validated by comparing the first question to a first question cluster in the question answering system, and it is determined that the first answer satisfies predefined security criteria. The first data set is evaluated to identify a set of references, and a generalized data set is generated by replacing each respective reference of the set of references with a corresponding entity identifier. The first generalized data set is then ingested into the question answering system.

Abstract: Techniques for automated training content generation are provided. A plurality of questions are retrieved, where each of the plurality of questions is associated with an answer in a plurality of answers. Further, it is determined that a first and a second answer in the plurality of answers are equivalent. A first question corresponding to the first answer and a second question corresponding to the second answer are identified, and a first question cluster including the first question and the second question is generated. The first question cluster is associated with at least one of the first answer and the second answer. Finally, upon determining that a number of questions in the plurality of questions that are included in the first question cluster exceeds a first predefined threshold, the first question cluster is ingested into a question answering system.

Abstract: Techniques for automated training content generation are provided. A plurality of questions are retrieved, where each of the plurality of questions is associated with an answer in a plurality of answers. Further, it is determined that a first and a second answer in the plurality of answers are equivalent. A first question corresponding to the first answer and a second question corresponding to the second answer are identified, and a first question cluster including the first question and the second question is generated. The first question cluster is associated with at least one of the first answer and the second answer. Finally, upon determining that a number of questions in the plurality of questions that are included in the first question cluster exceeds a first predefined threshold, the first question cluster is ingested into a question answering system.