ML Defect Assignment for Software Processing Performance Regressions
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Solution Overview
Problem
Existing file systems face challenges in efficiently identifying and assigning performance defects to software processing components, which often leads to inefficient resource utilization and prolonged debugging due to manual analysis of large data sets, incorrect identification of defective components, and time-consuming troubleshooting.
Innovation Solution
An automated method using a machine learning (ML) model to detect performance regressions by training on a subset of labeled performance features, reducing the training data through user selection and synthetic data generation, and accurately assigning defects to component specialists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of large data sets is used to identify performance defects, then comprehensive analysis can be performed, but it leads to prolonged debugging time and inefficient resource utilization
Solution Approach 1:
An automated defect identification system acts as an intermediary between performance data collection and manual analysis. The system automatically processes large datasets, identifies potential defects, and prioritizes them for review, reducing the time burden on manual analysts while maintaining identification accuracy through systematic automated screening followed by expert validation.
Solution Approach 2:
The system performs preliminary automated analysis of performance data before manual review, pre-identifying potential defects and organizing them by severity and likelihood. This preliminary action filters out obvious issues and prepares structured information for manual analysts, reducing the overall debugging time while preserving comprehensive analysis capabilities.
2Measurement precision
If manual analysis methods are used to troubleshoot performance issues, then detailed examination can be conducted, but it is time-consuming and leads to incorrect identification of defective components
Solution Approach 1:
The system implements feedback loops where automated identification results are continuously refined based on manual review outcomes. Defect identification algorithms learn from confirmed and rejected predictions, improving accuracy over time. This feedback mechanism reduces incorrect identifications while maintaining efficient automated processing.
Solution Approach 2:
Manual mechanical analysis methods are replaced with automated computational analysis systems that process performance data algorithmically. The system substitutes human manual inspection with automated defect detection algorithms, reducing troubleshooting time while improving consistency and accuracy through systematic data processing.
3Reliability
If comprehensive performance data is collected for analysis, then complete system coverage is achieved, but it increases data processing complexity and time requirements
Solution Approach 1:
Comprehensive performance data is segmented into distinct categories and components for separate processing. The system divides large datasets into manageable segments, processes them through specialized analysis modules, and integrates results. This segmentation maintains complete system coverage while reducing overall processing complexity through modular organization.
Solution Approach 2:
The system applies partial analysis to the most critical performance parameters first, focusing computational resources on high-impact data points. By performing excessive analysis on key metrics and selective analysis on secondary metrics, the system achieves reliable defect identification without processing every data point in exhaustive detail, thus reducing overall complexity.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, a system, and a non-transitory machine-readable medium for assigning performance defects to software processing components. Provided is a method that receives performance data of a set of performance runs of a software application across a set of software processing components of a file software system, where the performance data comprises multiple labeled performance features that are associated with the set of software processing components; receives, from a software processing component expert, a user-selection of a subset of labeled performance features of the several labeled performance features; and trains a machine learning (ML) model to determine whether one or more of the set of software processing components is associated with performance regressions of the software application, using the subset of labeled performance features of the performance data as training data.