Two-Tier AI Quality Gating for Code Intelligence Updates
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Solution Overview
Problem
Existing quality gating functions for code intelligence tools are unreliable and prone to bias, leading to potential degradation of tool performance due to undeservedly high ratings of updates, lacking scalable and robust evaluation methods.
Innovation Solution
Implementing a two-tier evaluation system with an evaluator and an evaluator tuner, utilizing a hierarchical quality criteria structure and human expertise, to assess and tune the evaluator for accurate scoring of code intelligence tool updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single-tier evaluation system is used to assess code intelligence tool updates, then the evaluation process is simple and fast, but the evaluation results are unreliable and biased
Solution Approach 1:
The evaluation system is divided into two distinct tiers: a first evaluation model that performs initial assessment and a second evaluation model that performs final assessment. This segmentation allows each model to specialize in different aspects of evaluation, improving overall reliability while maintaining manageable complexity through clear division of labor.
Solution Approach 2:
The first evaluation model serves as an intermediary between the code intelligence tool update and the second evaluation model. It performs preliminary filtering and assessment, providing refined input to the second model. This intermediary layer reduces the burden on the final evaluator and improves the quality of information passed along the evaluation chain.
2Productivity
If automated evaluation models are used to assess code intelligence tool updates, then the evaluation process is scalable, but the models are prone to bias and produce undeservedly high ratings
Solution Approach 1:
The system implements feedback loops where evaluation results from the first model inform the second model's assessment. The hierarchical structure allows higher-level evaluation criteria to guide lower-level assessments, creating a feedback mechanism that corrects biases and improves measurement precision while maintaining scalability through automated processing.
Solution Approach 2:
The evaluation system changes parameters by using different evaluation criteria and weighting schemes at different hierarchical levels. The first model uses initial parameters for broad assessment, while the second model adjusts parameters based on refined criteria, improving measurement precision without sacrificing the scalability of automated evaluation.
3Measurement precision
If comprehensive quality criteria are applied to evaluate code intelligence tool updates, then the evaluation is thorough and accurate, but the evaluation process becomes time-consuming
Solution Approach 1:
Comprehensive quality criteria are segmented and distributed across two evaluation tiers. The first model assesses certain criteria while the second model focuses on others, allowing thorough evaluation of multiple dimensions without requiring all criteria to be applied sequentially by a single model, thus reducing evaluation time while maintaining accuracy.
Solution Approach 2:
The first evaluation model performs preliminary assessment of quality criteria, identifying and filtering out clearly inadequate updates before they reach the second model. This preliminary action reduces the workload for comprehensive evaluation, maintaining measurement precision while significantly reducing the time loss associated with evaluating every update through the full criteria set.
Data Source
AI summary
Example solutions provide artificial intelligence (AI) annotated quality gating for code intelligence tools. A code intelligence tool update generates test results, which are evaluated by an evaluator, producing evaluation results. A quality score is determined for the evaluation results using a hierarchical quality criteria structure. This enables determination of whether the evaluator is providing a proper assessment of the code intelligence tool update (or may instead be rating the code intelligence tool update undeservedly high). If the evaluator is not providing a proper assessment of the code intelligence tool update, there is a risk that an underperforming update may be performed on the currently operational code intelligence tool, degrading the performance. The evaluator is tuned to improve the quality score. Using this two-tier assessment approach permits robust scaling. Human intervention is performed on the evaluator, which in turn handles the larger task of evaluating code intelligence tool updates.