A mechanism is provided in a data processing system for performing regression testing on a question answering system instance. The mechanism trains a machine learning model for a question answering system using a ground truth virtual checksum as part of a ground truth including domain-specific ground truth. The ground truth virtual checksum comprises a set of test questions, an answer to each test question, and a confidence level range for each answer to a corresponding test question. The mechanism runs regression test buckets across system nodes with domain-specific corpora and receiving results from the system nodes. Each system node implements a question answering system instance of the question answering system by executing in accordance with the machine learning model and by accessing domain-specific corpora. Each test bucket includes a set of questions matching a subset of questions in the ground truth virtual checksum. The mechanism identifies regressions, inconsistencies, or destabilizations in code behavior in the system nodes based on results of comparing the results to the ground truth virtual checksum and generates a report presenting the identified regressions, inconsistencies, or destabilizations and the affected system nodes.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, in a data processing system, for performing regression testing on a question answering system instance, the method comprising: training a machine learning model for a question answering system using a ground truth virtual checksum as part of a ground truth including domain-specific ground truth, wherein the ground truth virtual checksum comprises a set of test questions, an answer to each test question, and a confidence level range for each answer to a corresponding test question; running regression test buckets across system nodes with domain-specific corpora and receiving results from the system nodes, wherein each system node implements a question answering system instance of the question answering system by executing in accordance with the machine learning model and by accessing domain-specific corpora and wherein each test bucket includes a set of questions matching a subset of questions in the ground truth virtual checksum; and identifying regressions, inconsistencies, or destabilizations in code behavior in the system nodes based on results of comparing the results to the ground truth virtual checksum; and generating a report presenting the identified regressions, inconsistencies, or destabilizations and the affected system nodes. 2. The method of claim 1, wherein running test buckets across system nodes comprises calling a test script in a given system node to retrieve a designated question from a designated test bucket, submit the designated question to the question answering system instance at the given system node, and receive a result answer and a result confidence value from the question answering system instance. 3. The method of claim 2, wherein the ground truth virtual checksum further comprises a performance metric value range for each test question. 4. The method of claim 3, wherein the performance metric value range comprises a response time range. 5. The method of claim 3, wherein the question answering system instance returns a result performance metric value and wherein identifying regressions, inconsistencies, or destabilizations comprises comparing the result answer, the result confidence value, and the result performance metric value to the ground truth virtual checksum. 6. The method of claim 2, wherein the ground truth virtual checksum is a multi-dimensional ground truth virtual checksum comprising a question identifier and a plurality of three-value n-tuples, each comprising an answer identifier, a confidence level range, and a response time range. 7. The method of claim 6, wherein the question answering system instance returns a plurality of candidate answers, each having an associated answer identifier, confidence value, and performance metric value, and wherein identifying regressions, inconsistencies, or destabilizations comprises comparing the answer identifier, confidence value, and performance metric value of the plurality of candidate answers to the ground truth virtual checksum. 8. The method of claim 1, wherein each test question is selected to exercise predefined components of a question answering pipeline of the question answering system instance. 9. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: train a machine learning model for a question answering system using a ground truth virtual checksum as part of a ground truth including domain-specific ground truth, wherein the ground truth virtual checksum comprises a set of test questions, an answer to each test question, and a confidence level range for each answer to a corresponding test question; run regression test buckets across system nodes with domain-specific corpora and receiving results from the system nodes, wherein each system node implements a question answering system instance of the question answering system by executing in accordance with the machine learning model and by accessing domain-specific corpora and wherein each test bucket includes a set of questions matching a subset of questions in the ground truth virtual checksum; and identify regressions, inconsistencies, or destabilizations in code behavior in the system nodes based on results of comparing the results to the ground truth virtual checksum; and generate a report presenting the identified regressions, inconsistencies, or destabilizations and the affected system nodes. 10. The computer program product of claim 9, wherein running test buckets across system nodes comprises calling a test script in a given system node to retrieve a designated question from a designated test bucket, submit the designated question to the question answering system instance at the given system node, and receive a result answer and a result confidence value from the question answering system instance. 11. The computer program product of claim 10, wherein the ground truth virtual checksum further comprises a performance metric value range for each test question. 12. The computer program product of claim 11, wherein the performance metric value range comprises a response time range. 13. The computer program product of claim 11, wherein the question answering system instance returns a result performance metric value and wherein identifying regressions, inconsistencies, or destabilizations comprises comparing the result answer, the result confidence value, and the result performance metric value to the ground truth virtual checksum. 14. The computer program product of claim 10, wherein the ground truth virtual checksum is a multi-dimensional ground truth virtual checksum comprising a question identifier and a plurality of three-value n-tuples, each comprising an answer identifier, a confidence level range, and a response time range. 15. The computer program product of claim 14, wherein the question answering system instance returns a plurality of candidate answers, each having an associated answer identifier, confidence value, and performance metric value, and wherein identifying regressions, inconsistencies, or destabilizations comprises comparing the answer identifier, confidence value, and performance metric value of the plurality of candidate answers to the ground truth virtual checksum. 16. The computer program product of claim 9, wherein each test question is selected to exercise predefined components of a question answering pipeline of the question answering system instance. 17. An apparatus comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to: train a machine learning model for a question answering system using a ground truth virtual checksum as part of a ground truth including domain-specific ground truth, wherein the ground truth virtual checksum comprises a set of test questions, an answer to each test question, and a confidence level range for each answer to a corresponding test question; run regression test buckets across system nodes with domain-specific corpora and receiving results from the system nodes, wherein each system node implements a question answering system instance of the question answering system by executing in accordance with the machine learning model and by accessing domain-specific corpora and wherein each test bucket includes a set of questions matching a subset of questions in the ground truth virtual checksum; and identify regressions, inconsistencies, or destabilizations in code behavior in the system nodes based on results of comparing the results to the ground truth virtual checksum; and generate a report presenting the identified regressions, inconsistencies, or destabilizations and the affected system nodes. 18. The apparatus of claim 17, wherein running test buckets across system nodes comprises calling a test script in a given system node to retrieve a designated question from a designated test bucket, submit the designated question to the question answering system instance at the given system node, and receive a result answer and a result confidence value from the question answering system instance. 19. The apparatus of claim 18, wherein the ground truth virtual checksum further comprises a performance metric value range for each test question. 20. The apparatus of claim 18, wherein the ground truth virtual checksum is a multi-dimensional ground truth virtual checksum comprising a question identifier and a plurality of three-value n-tuples, each comprising an answer identifier, a confidence level range, and a response time range.