⚡️ Speed up function is_test_file by 17% in PR #1199 (omni-java)#1373
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codeflash-ai[bot] wants to merge 1 commit intoomni-javafrom
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⚡️ Speed up function is_test_file by 17% in PR #1199 (omni-java)#1373codeflash-ai[bot] wants to merge 1 commit intoomni-javafrom
is_test_file by 17% in PR #1199 (omni-java)#1373codeflash-ai[bot] wants to merge 1 commit intoomni-javafrom
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The optimized code achieves a **16% runtime improvement** by reducing per-call overhead through two key optimizations:
## What Changed
1. **Module-level constants**: The tuples `("Test.java", "Tests.java")` and `("test", "tests", "src/test")` are now defined once as module-level constants (`_TEST_NAME_SUFFIXES` and `_TEST_DIRS` frozenset) instead of being recreated on every function call.
2. **Explicit loop vs. generator**: Replaced `any(part in (...) for part in path_parts)` with an explicit `for` loop that returns `True` immediately upon finding a match, avoiding generator object creation overhead.
## Why This Is Faster
**Constant reuse eliminates repeated allocations**: In the original code, Python creates new tuple objects for `("Test.java", "Tests.java")` and `("test", "tests", "src/test")` on every function invocation. With 2,851 calls in the profiler trace, this means ~5,700 tuple allocations. The optimized version defines these once at module load time, eliminating this overhead entirely.
**Explicit loops reduce Python interpreter overhead**: The `any()` builtin with a generator expression involves:
- Creating a generator object
- Iterator protocol overhead (calling `__next__` repeatedly)
- Exception handling when the generator exhausts
An explicit `for` loop with early return is more direct and avoids generator object allocation. The line profiler confirms this: the original's `any()` line took 2.47ms total, while the optimized explicit loop operations take 1.08ms + 0.92ms = 2.0ms total—a measurable improvement.
**Frozenset lookups are optimized**: Converting the test directory names to a `frozenset` enables O(1) average-case membership testing versus linear scanning through a tuple.
## Performance Characteristics
The annotated tests reveal this optimization particularly excels when:
- **Directory checking dominates** (25-50% speedups): Cases like `Path("project/test/com/Example.java")` show 25.6% improvement because the directory check now benefits from both the frozenset lookup and explicit loop efficiency
- **Deep path traversal** (30-46% speedups): Paths like `Path("a/b/c/d/e/f/test/MyClass.java")` gain 36% because the explicit loop can exit early once 'test' is found
- **Non-test files** (11-20% speedups): Even paths that must fully traverse all parts benefit from reduced overhead
The optimization shows slight regressions (1-9% slower) in simple naming pattern cases like `Path("Test.java")` because the constant lookup adds minimal overhead for already-fast operations, but these are rare and the overall workload shows strong net improvement.
## Impact on Existing Workloads
Based on `function_references`, this function is called from test discovery code paths that process potentially hundreds or thousands of files when scanning Java projects. The function determines whether files should be included in test suites, making it a hot path during:
- Project-wide test discovery
- Build system integration
- IDE test runner initialization
The 16% runtime reduction directly translates to faster test discovery, which is valuable in CI/CD pipelines and developer workflows where test scanning happens frequently. The optimization is especially beneficial for large Java codebases with deep directory structures, as evidenced by the 30-50% improvements on nested path cases.
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⚡️ This pull request contains optimizations for PR #1199
If you approve this dependent PR, these changes will be merged into the original PR branch
omni-java.📄 17% (0.17x) speedup for
is_test_fileincodeflash/languages/java/test_discovery.py⏱️ Runtime :
609 microseconds→521 microseconds(best of220runs)📝 Explanation and details
The optimized code achieves a 16% runtime improvement by reducing per-call overhead through two key optimizations:
What Changed
Module-level constants: The tuples
("Test.java", "Tests.java")and("test", "tests", "src/test")are now defined once as module-level constants (_TEST_NAME_SUFFIXESand_TEST_DIRSfrozenset) instead of being recreated on every function call.Explicit loop vs. generator: Replaced
any(part in (...) for part in path_parts)with an explicitforloop that returnsTrueimmediately upon finding a match, avoiding generator object creation overhead.Why This Is Faster
Constant reuse eliminates repeated allocations: In the original code, Python creates new tuple objects for
("Test.java", "Tests.java")and("test", "tests", "src/test")on every function invocation. With 2,851 calls in the profiler trace, this means ~5,700 tuple allocations. The optimized version defines these once at module load time, eliminating this overhead entirely.Explicit loops reduce Python interpreter overhead: The
any()builtin with a generator expression involves:__next__repeatedly)An explicit
forloop with early return is more direct and avoids generator object allocation. The line profiler confirms this: the original'sany()line took 2.47ms total, while the optimized explicit loop operations take 1.08ms + 0.92ms = 2.0ms total—a measurable improvement.Frozenset lookups are optimized: Converting the test directory names to a
frozensetenables O(1) average-case membership testing versus linear scanning through a tuple.Performance Characteristics
The annotated tests reveal this optimization particularly excels when:
Path("project/test/com/Example.java")show 25.6% improvement because the directory check now benefits from both the frozenset lookup and explicit loop efficiencyPath("a/b/c/d/e/f/test/MyClass.java")gain 36% because the explicit loop can exit early once 'test' is foundThe optimization shows slight regressions (1-9% slower) in simple naming pattern cases like
Path("Test.java")because the constant lookup adds minimal overhead for already-fast operations, but these are rare and the overall workload shows strong net improvement.Impact on Existing Workloads
Based on
function_references, this function is called from test discovery code paths that process potentially hundreds or thousands of files when scanning Java projects. The function determines whether files should be included in test suites, making it a hot path during:The 16% runtime reduction directly translates to faster test discovery, which is valuable in CI/CD pipelines and developer workflows where test scanning happens frequently. The optimization is especially beneficial for large Java codebases with deep directory structures, as evidenced by the 30-50% improvements on nested path cases.
✅ Correctness verification report:
🌀 Click to see Generated Regression Tests
To edit these changes
git checkout codeflash/optimize-pr1199-2026-02-04T07.10.04and push.