Profile-Guided Optimization in JavaScript: Simulating Hotspot Identification

Profile-guided optimization (PGO) is a powerful technique used in compilers to optimize code based on runtime behavior. This challenge simulates a simplified version of PGO in JavaScript, focusing on identifying "hotspots" – frequently executed code sections – and applying a basic optimization strategy. The goal is to analyze a provided function's execution profile and then modify the function to potentially improve performance by prioritizing frequently used code paths.

Problem Description

You are given a JavaScript function and a corresponding execution profile. The profile is a simple object where keys are lines of code within the function (represented as strings) and values are the number of times that line was executed during profiling. Your task is to analyze this profile, identify the top N "hotspot" lines of code, and then modify the original function to prioritize execution along those lines. For this challenge, "prioritization" means wrapping the hotspot lines in a function that is called first. This simulates a compiler potentially inlining or optimizing those lines.

What needs to be achieved:

1. Profile Analysis: Determine the top N most frequently executed lines of code from the provided profile.
2. Function Modification: Wrap the top N lines of code within a new function.
3. Integration: Replace the original lines of code in the function with calls to this new function.

Key Requirements:

• The function to be optimized will be provided as a string.
• The execution profile will be provided as an object.
• The value of N (the number of hotspots to prioritize) will be provided.
• The function modification must preserve the original function's functionality.
• The solution should handle cases where the profile is empty or N is larger than the number of lines in the profile.

Expected Behavior:

The function should return a modified JavaScript function string where the top N hotspot lines are wrapped in a new function. The original function's logic should remain intact, but the execution flow should be altered to prioritize the hotspot lines.

Edge Cases to Consider:

• Empty profile: Return the original function unchanged.
• N is 0: Return the original function unchanged.
• N is greater than the number of lines in the profile: Wrap all lines in a new function.
• Lines of code containing special characters (e.g., quotes, semicolons) should be handled correctly.
• The function string may contain multi-line statements.

Examples

Example 1:

Input:
functionString: "function foo() { let x = 1; x = x + 1; return x; }"
profile: { "let x = 1;", "x = x + 1;", "return x;" }
N: 2
Output:
"function foo() { function hotspot(x) { let x = 1; x = x + 1; return x; } return hotspot(1); }"
Explanation: The lines "let x = 1;" and "x = x + 1;" are the hotspots. They are wrapped in a new function called `hotspot`. The original function now calls `hotspot` with the initial value of 1.

Example 2:

Input:
functionString: "function bar() { console.log('hello'); console.log('world'); }"
profile: { "console.log('hello');", "console.log('world');" }
N: 1
Output:
"function bar() { function hotspot() { console.log('hello'); } return hotspot(); console.log('world'); }"
Explanation: "console.log('hello');" is the hotspot. It's wrapped in a function called `hotspot`. The original function now calls `hotspot` and then executes `console.log('world');`.

Example 3: (Edge Case)

Input:
functionString: "function baz() { return 1; }"
profile: {}
N: 1
Output:
"function baz() { return 1; }"
Explanation: The profile is empty, so the original function is returned unchanged.

Constraints

• functionString will be a valid JavaScript function string.
• profile will be an object where keys are strings representing lines of code and values are numbers representing execution counts.
• N will be a non-negative integer.
• The length of functionString will be less than 1000 characters.
• The number of lines in the profile will be less than 50.
• Performance: The solution should complete within 1 second for the given constraints.

Notes

• This is a simplified simulation of PGO. Real PGO involves more complex analysis and optimization techniques.
• The line numbers in the profile are not necessarily sequential.
• The function modification is a basic form of prioritization. More sophisticated techniques could involve inlining, loop unrolling, or other optimizations.
• Focus on correctly identifying the hotspots and wrapping them in a new function. The exact implementation of the new function's name and arguments is not critical, as long as the original function's logic is preserved.
• Consider using regular expressions to split the function string into lines. Be mindful of multi-line statements.
• The order of lines in the profile is not guaranteed. Sort the profile by execution count to identify the hotspots.