⚡️ Speed up function is_inside_string by 58% in PR #1318 (fix/js-jest30-loop-runner)#1467
⚡️ Speed up function is_inside_string by 58% in PR #1318 (fix/js-jest30-loop-runner)#1467codeflash-ai[bot] wants to merge 2 commits intomainfrom
is_inside_string by 58% in PR #1318 (fix/js-jest30-loop-runner)#1467Conversation
The optimized code achieves a **58% runtime improvement** (from 991μs to 627μs) by replacing character-by-character iteration with a **regex-based fast-path** that jumps directly to the next "special" character (quotes or backslashes). **Key optimization:** Instead of examining every character in the code string with Python-level operations (`code[i]`, `char in "\"'`"`), the optimized version uses a precompiled regex pattern (`_SPECIAL_RE`) to scan for the next relevant character in C code (via the regex engine). This dramatically reduces Python interpreter overhead. **Why this works:** - The original code spent ~16% of time in the `while i < pos` loop condition checks and ~17.8% indexing into the string (`char = code[i]`) - The optimized code reduces loop iterations from 21,270 to just 1,859 by skipping over long stretches of non-special characters - For long strings without quotes/backslashes, `search()` can scan hundreds/thousands of characters in a single C-level operation instead of iterating in Python **Performance characteristics based on test results:** - **Small strings (< 20 chars)**: Actually 50-70% slower due to regex overhead - the setup cost of calling `search()` outweighs the benefit - **Large strings (> 1000 chars)**: Massive speedups of 500-1300% - the fast-path shines when scanning long runs of normal code - **Medium strings (20-1000 chars)**: Mixed results - slight slowdowns to moderate gains depending on quote density **Impact on workloads:** The function is called from test instrumentation code (as shown in `test_javascript_instrumentation.py`), where it checks if positions in JavaScript code are inside string literals. In real-world instrumentation scenarios with typical JavaScript files (hundreds to thousands of characters), this optimization will significantly reduce overhead when instrumenting or analyzing code, especially when checking many positions in files with long non-string sections. The trade-off of slower performance on very small strings is acceptable because the absolute time difference (nanoseconds vs microseconds) is negligible, while the gains on realistically-sized code files are substantial.
PR Review SummaryPrek ChecksPassed after auto-fix. Ruff format fixed 2 extra blank lines in Mypy ChecksAll 5 mypy errors found are pre-existing on main (missing Code ReviewNo critical issues found. The
Note: This PR is auto-generated by codeflash-ai and targets the feature branch Test Coverage
Coverage analysis:
9 pre-existing test failures in Last updated: 2026-02-12 |
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⚡️ This pull request contains optimizations for PR #1318
If you approve this dependent PR, these changes will be merged into the original PR branch
fix/js-jest30-loop-runner.📄 58% (0.58x) speedup for
is_inside_stringincodeflash/languages/javascript/instrument.py⏱️ Runtime :
991 microseconds→627 microseconds(best of140runs)📝 Explanation and details
The optimized code achieves a 58% runtime improvement (from 991μs to 627μs) by replacing character-by-character iteration with a regex-based fast-path that jumps directly to the next "special" character (quotes or backslashes).
Key optimization:
Instead of examining every character in the code string with Python-level operations (
code[i],char in "\"'"), the optimized version uses a precompiled regex pattern (_SPECIAL_RE`) to scan for the next relevant character in C code (via the regex engine). This dramatically reduces Python interpreter overhead.Why this works:
while i < posloop condition checks and ~17.8% indexing into the string (char = code[i])search()can scan hundreds/thousands of characters in a single C-level operation instead of iterating in PythonPerformance characteristics based on test results:
search()outweighs the benefitImpact on workloads:
The function is called from test instrumentation code (as shown in
test_javascript_instrumentation.py), where it checks if positions in JavaScript code are inside string literals. In real-world instrumentation scenarios with typical JavaScript files (hundreds to thousands of characters), this optimization will significantly reduce overhead when instrumenting or analyzing code, especially when checking many positions in files with long non-string sections.The trade-off of slower performance on very small strings is acceptable because the absolute time difference (nanoseconds vs microseconds) is negligible, while the gains on realistically-sized code files are substantial.
✅ Correctness verification report:
🌀 Click to see Generated Regression Tests
To edit these changes
git checkout codeflash/optimize-pr1318-2026-02-12T16.29.21and push.