Software Performance Regression Prediction Using Static Analysis
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Solution Overview
Problem
Detecting and predicting performance regression in computing systems is challenging due to the complexity of software and hardware transformations, which can lead to operational inefficiencies and resource misuse, particularly in applications like AI and HPC workloads, making it difficult to identify the root cause and impact on system performance.
Innovation Solution
A system utilizing static analysis metrics and hardware features, combined with application profiling data, builds statistical performance models using machine learning algorithms to predict performance impacts from software modifications, allowing for automated detection and prediction of performance regression without extensive real-world testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequent experimentation and tracking of changes to hardware and software features is performed to detect performance regression, then detection accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The system performs static analysis on software code before actual execution to extract software features and build performance models in advance. This preliminary action enables the system to predict performance regression without requiring frequent real-world experimentation and tracking, thereby improving detection accuracy while reducing time consumption.
2Loss of time
If static analysis and machine learning models are used to predict performance regression, then time consumption is reduced, but measurement precision may deteriorate
Solution Approach 1:
The system introduces hardware features as an intermediary between static analysis metrics and performance regression detection. By combining software static analysis metrics with hardware features through machine learning models, the system achieves both rapid detection and high accuracy, as hardware features provide additional contextual information that enhances prediction precision.
3Measurement precision
If extensive real-world testing is performed to detect performance regression, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The system replaces the mechanical process of extensive real-world testing with a computational approach using static analysis and machine learning models. By substituting physical experimentation with algorithmic prediction, the system maintains high detection accuracy while significantly improving software development efficiency and productivity.
4Measurement precision
If comprehensive hardware and software monitoring is implemented, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The system extracts only the most relevant software features and hardware features that have the greatest impact on performance regression. By selectively extracting and analyzing only the critical features rather than monitoring all possible parameters, the system achieves high detection accuracy while minimizing system complexity.
Data Source
AI summary
In certain implementations, a system includes one or more processors and a storage storing a program for execution by the one or more processors. The program includes instructions to parse a software application to extract a semantic structure and derive static analysis metrics; collect application profiling data to detect code regions responsible for performance of the software application; and output, based on metrics of a first transformation of the software application, a variance observed during a performance simulation. The metrics of the first transformation of the software application are derived from the static analysis metrics and the detected code regions responsible for performance of the software application.