Runtime Visualization for Executable Code Performance Analysis
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Solution Overview
Problem
Current software development methods lack effective tools for visualizing and analyzing the runtime behavior and efficiency of executable code, forcing developers to guess or infer performance issues without comprehensive insights.
Innovation Solution
A method and apparatus for generating visualizations from running executable code by monitoring runtime events, capturing profile data, and rendering visualizations that display function call hierarchies, execution times, and performance metrics, using an integrated development environment with an application profiler and data store.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If developers rely on traditional compilation and execution without visualization tools, then the development process remains simple and fast, but developers cannot obtain comprehensive insights into runtime behavior and performance characteristics
Solution Approach 1:
The patent introduces a visualization system as an intermediary between the executable code and the developer. This system captures runtime events, generates profiles, and renders visualizations that mediate the information flow, allowing developers to obtain comprehensive runtime insights without directly analyzing complex execution traces themselves.
Solution Approach 2:
The system creates a profile representation that copies essential runtime behavior characteristics from the actual execution. Instead of requiring developers to analyze the full execution trace, the system generates a simplified profile data structure that preserves key performance information while being much easier to process and visualize.
2Productivity
If developers manually analyze runtime performance by guessing and gleaning from execution, then no additional tools are needed, but performance optimization becomes time-consuming and less accurate
Solution Approach 1:
The system performs preliminary capture and processing of runtime events during execution, automatically generating profiles and visualizations before the developer needs to analyze performance. This preliminary action eliminates the need for manual analysis during development, significantly improving productivity while reducing the time required for optimization.
Solution Approach 2:
The visualization system provides immediate feedback to developers about runtime performance characteristics, execution times, and resource usage. This feedback mechanism allows developers to quickly identify performance bottlenecks and optimize their code without spending extensive time on manual analysis, thereby improving productivity and reducing optimization time.
3Measurement precision
If comprehensive runtime monitoring and visualization are implemented, then detailed performance insights are obtained, but system complexity and overhead increase
Solution Approach 1:
The system extracts only the essential performance information from runtime events, separating useful measurements from unnecessary data. By taking out only the critical metrics (execution times, resource usage, event counts), the system achieves precise performance measurement while keeping the monitoring overhead and complexity manageable.
Solution Approach 2:
Instead of monitoring every possible runtime detail, the system implements partial monitoring focused on key performance indicators. This partial action approach provides sufficient measurement precision for performance optimization while avoiding the excessive complexity and overhead that would result from comprehensive monitoring of all execution details.
Data Source
AI summary
A method and apparatus for generating visualizations from running executable code is described. In one embodiment, the method includes monitoring an executable software application while the software application is running. The method may also include capturing data indicative of runtime events that occur while the executable software application is run. In one embodiment, the method may further include generating a profile of the executable software application from the captured data, and rendering a visualization of the profile for display.