Ground Truth Virtual Checksums for Question Answering Regression Testing
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Solution Overview
Problem
Complex question answering systems face challenges in scalable and repeatable regression testing due to diverse customer domains and unique customer-maintained corpora, making it difficult to ensure consistent performance and functionality across multiple instances.
Innovation Solution
The implementation of a ground truth virtual checksum technique, which includes a predefined set of question and answer pairs with response characteristics and metadata, allows for scalable regression testing by exercising logic paths across all system nodes with domain-specific corpora, identifying regressions, inconsistencies, or destabilizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional regression testing methods are used on question answering systems with diverse customer domains and unique corpora, then each system instance can be tested individually, but it becomes difficult to ensure consistent performance and functionality across multiple instances and scaling becomes challenging
Solution Approach 1:
The patent creates a universal regression testing framework that works across multiple question answering system instances with different customer domains and corpora. The virtual checksum technique provides a common testing mechanism that can be applied universally to verify system integrity across diverse environments, ensuring consistent performance while accommodating domain-specific variations.
Solution Approach 2:
The patent changes the testing approach from domain-specific custom tests to a parameter-based virtual checksum verification. By representing system state as configurable parameters (checksum values) that can be computed and verified independently of specific domain content, the system achieves scalable regression testing across multiple instances without requiring complex domain-specific test cases.
2Productivity
If custom regression tests are created for each customer domain with unique corpora, then domain-specific functionality can be verified, but the testing process becomes time-consuming and difficult to scale across multiple system instances
Solution Approach 1:
The patent uses virtual checksums as simplified copies or representations of the actual system state and corpora. Instead of copying and testing entire domain-specific corpora across all instances, the system computes checksum values that represent the essential state, enabling rapid verification without the time cost of processing actual domain content at each testing instance.
Solution Approach 2:
The patent performs preliminary computation of virtual checksums during system initialization or configuration phases. By pre-computing these verification values before regression testing begins, the actual testing process becomes much faster, as it only requires comparing current state against the pre-established checksum benchmarks rather than executing complex domain-specific test suites.
3Measurement precision
If comprehensive regression testing is performed across all system nodes with domain-specific corpora, then code coverage and verification are improved, but the testing process becomes complex and difficult to manage
Solution Approach 1:
The patent introduces virtual checksums as intermediary representations between the actual system code/corpora and the regression testing process. These checksum values serve as mediators that capture essential system state information in a simplified, comparable format, enabling comprehensive verification across system nodes without the complexity of managing detailed domain-specific test cases for each node.
Data Source
AI summary
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.


