UIMA Pipeline Instrumentation for Annotator Testing
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Solution Overview
Problem
Developers of Natural Language Processing (NLP)-based systems face productivity issues due to the time-consuming and resource-intensive process of initializing and running full UIMA pipelines, which requires restarting the entire pipeline for even minor changes, leading to inefficient testing and development.
Innovation Solution
The system instruments a running pipeline to capture static configuration and dynamic event data, generating a reusable data model that allows for simulated testing and modification of Annotators without re-initializing the full pipeline, enabling efficient replay and simulation of pipeline scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a full UIMA pipeline is re-initialized and executed every time an Annotator is modified, then testing accuracy is improved, but development time and resource consumption increase significantly
Solution Approach 1:
The patent segments the full pipeline execution into individual Annotator testing units. Instead of re-running the entire pipeline for each Annotator modification, the system isolates and tests only the modified Annotator component, thereby reducing development time while maintaining testing accuracy for the specific component being developed.
Solution Approach 2:
The patent performs preliminary actions by pre-configuring and caching pipeline components, descriptors, and execution environments before actual testing begins. This preliminary setup allows rapid iteration on individual Annotators without repeatedly initializing the full pipeline, thus reducing development time while preserving testing reliability.
2Reliability
If a full UIMA pipeline is re-initialized for every Annotator change, then testing completeness is improved, but computational resources are wasted
Solution Approach 1:
The patent extracts and isolates the specific Annotator component that needs testing from the full pipeline context. By taking out only the relevant Annotator and its immediate dependencies for testing, the system achieves testing completeness for the modified component while avoiding the computational overhead of re-initializing and running the entire pipeline.
Solution Approach 2:
The patent applies partial action by executing only the necessary portion of the pipeline required to test the modified Annotator, rather than running the complete pipeline. This partial execution approach ensures testing completeness for the specific component while significantly reducing computational resource consumption.
3Productivity
If developers work on individual Annotators in isolation, then development efficiency is improved, but integration issues are not detected until full pipeline testing
Solution Approach 1:
The patent implements feedback mechanisms that allow developers to test individual Annotators in isolation with immediate results, while also providing integration testing capabilities. The system feeds back integration test results to developers, allowing them to detect and fix integration issues early in the development process rather than waiting until final full pipeline testing.
Solution Approach 2:
The patent creates a dynamic testing environment where the pipeline configuration can be flexibly adjusted to test individual Annotators or groups of Annotators as needed. This dynamic approach allows developers to work efficiently on individual components while maintaining the ability to perform integration testing when required, thus balancing development efficiency with integration reliability.
Data Source
AI summary
The technique herein substantially improves productivity of Annotator developers by providing methods and systems to develop and test Annotators without having to run a full pipeline every time changes are made to a particular Annotator. To this end, preferably a running pipeline is instrumented to enable automated recording of static configuration and dynamically-generated event data as the pipeline is executed. Based on these data, a reusable data model is generated that captures code and other dependencies in the pipeline (e.g., configuration parameters, intermediary CASes, program flow, annotations, and the like). The data model is then used to facilitate testing of Annotators without using the full pipeline (or even major sub-pipelines therein).