Profile Data Mutator for Script Execution Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Scripts written in dynamic programming languages, such as ECMAScript, face inefficiencies due to the parsing of entire code bases at startup, leading to delayed execution and suboptimal performance, as most code does not exploit the language's full flexibility, and existing optimization systems rely on historical profile data that may not represent future script behavior.
Innovation Solution
A system comprising a runtime engine and a profile data mutator that collects profile data during script execution, performs JIT compilation of selected portions, and modifies profile data entities to facilitate testing, ensuring optimizations are robust across changing script behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire code base is parsed at startup, then the script is prepared for execution, but execution start is delayed
Solution Approach 1:
The patent divides the script code base into multiple segments or portions, parsing and preparing only the necessary segments at startup rather than the entire code base. This segmentation allows selective compilation and execution of code portions, reducing startup time while maintaining execution readiness for critical functions.
Solution Approach 2:
The patent performs preliminary parsing and compilation actions on selected portions of the script before execution starts, rather than waiting until runtime. By pre-processing only the necessary code segments that will be executed during startup, the system prepares the script adequately while avoiding the time cost of processing the entire code base.
2Productivity
If optimizations are applied based on historical profile data, then execution efficiency is improved, but correctness may be compromised when script behavior changes
Solution Approach 1:
The patent implements a feedback mechanism where the system monitors script execution and detects when actual behavior deviates from the historical profile data. When such deviations are detected, the system can adjust or invalidate the applied optimizations, ensuring correctness is maintained even when script behavior changes from the historical pattern.
Solution Approach 2:
The patent makes the optimization system dynamic by allowing it to adapt based on runtime observations. Rather than applying static optimizations based solely on historical data, the system continuously evaluates whether the observed script behavior matches the profile, and dynamically adjusts optimization application accordingly to maintain correctness.
3Productivity
If profile data is collected during script execution, then optimization opportunities are identified, but the profile data may not represent future script behavior
Solution Approach 1:
The patent creates a testing system that serves multiple functions: it not only collects profile data for optimization but also mutates the profile data to generate test cases that verify optimization correctness under varied conditions. This multi-functional approach allows the system to both identify optimization opportunities and validate their robustness against behavioral variations.
Solution Approach 2:
The patent applies parameter changes to the profile data by mutating various parameters within the profile to create test scenarios. This transformation allows the system to test how optimizations perform when profile parameters differ from historical values, thereby assessing the representativeness and robustness of the collected profile data.
Data Source
AI summary
Systems, methods and computer program products are described herein for testing a system that is designed to optimize the execution of code within an application or other computer program based on profile data collected during the execution of such code. The embodiments described herein utilize what is referred to as a “profile data mutator” to mutate or modify the profile data between the point when it is collected and the point when it is used to apply an optimization. By mutating the profile data at this point, testing of a system for optimized code execution can be significantly more thorough. Furthermore, such profile data mutation leads to a more scalable and efficient testing technique for profile-guided systems for optimized code execution.