Optimize JavaLineProfiler.instrument_function

Runtime improvement: the optimized version runs ~25% faster (2.06 ms -> 1.64 ms), with larger wins on functions with many instrumentable lines.

What changed
- Precompute file_path.as_posix() once per function: file_posix = file_path.as_posix() and reuse it for content_key and the profiled f-string instead of calling file_path.as_posix() repeatedly inside the loop.
- Combined multiple startswith checks into a single startswith(("//", "/*", "*")) call to replace three separate str.startswith() calls.

Why this speeds things up
- Attribute lookups and small method calls are relatively expensive in Python. In the original code file_path.as_posix() was called twice for every instrumented line (once for content_key and once inside the profiled f-string). Moving that result into a local variable removes those repeated method calls and attribute lookups and replaces them with a fast local variable load.
- Multiple str.startswith() calls were replaced by one startswith(tuple) call, cutting the number of Python-level function calls and condition checks for candidate comment lines.
- These savings multiply with the number of lines inside a function. The loop is the hot path: each instrumented statement previously did several extra method calls and string operations; removing them reduces per-line overhead and thus the total runtime.

Observed evidence
- Line-profiling shows heavy time spent on the two expressions that used file_path.as_posix() repeatedly; those costs drop in the optimized profile.
- The annotated tests show the biggest improvements on the large-scale test (many lines), which matches the expectation that per-line micro-optimizations are most beneficial when the loop has many iterations.

Behavioral impact and trade-offs
- No functional change: the instrumented output and semantics are unchanged.
- A tiny upfront cost (computing file_posix once) is paid even if no lines are instrumented, but that's negligible and worth the per-line savings.
- The exception path (logger.warning) shows a larger percent of the shorter total time in the optimized profile — this is not a regression in practice, just a profiling artifact because the overall runtime decreased; exception handling remains unchanged.

When this matters most
- Hot paths that instrument long functions or many functions (the large-scale test) gain the most.
- Small functions still benefit (the overall runtime measured improved), but the relative improvement is smaller because fixed costs (parse, etc.) dominate.

Summary
Precomputing file_posix and reducing redundant startswith/attribute calls cut down repeated Python-level work inside the main loop. That directly lowers per-line overhead and yields the measured ~25% runtime improvement, especially on workloads with many instrumentable lines.

Author: codeflash-ai[bot]

codeflash/languages/java/line_profiler.py

@@ -353,6 +353,8 @@ def instrument_function(self, func: FunctionInfo, lines: list[str], file_path: P
         # Add profiling to each executable line
         function_entry_added = False
 
+        file_posix = file_path.as_posix()
+
         for local_idx, line in enumerate(func_lines):
             local_line_num = local_idx + 1  # 1-indexed within function
             global_line_num = func.starting_line + local_idx  # Global line number
@@ -377,23 +379,19 @@ def instrument_function(self, func: FunctionInfo, lines: list[str], file_path: P
             if (
                 local_line_num in executable_lines
                 and stripped
-                and not stripped.startswith("//")
-                and not stripped.startswith("/*")
-                and not stripped.startswith("*")
+                and not stripped.startswith(("//", "/*", "*"))
                 and stripped not in ("}", "};")
             ):
                 # Get indentation
                 indent = len(line) - len(line.lstrip())
                 indent_str = " " * indent
 
                 # Store line content for profiler output
-                content_key = f"{file_path.as_posix()}:{global_line_num}"
+                content_key = f"{file_posix}:{global_line_num}"
                 self.line_contents[content_key] = stripped
 
                 # Add hit() call before the line
-                profiled_line = (
-                    f'{indent_str}{self.profiler_class}.hit("{file_path.as_posix()}", {global_line_num});\n{line}'
-                )
+                profiled_line = f'{indent_str}{self.profiler_class}.hit("{file_posix}", {global_line_num});\n{line}'
                 instrumented_lines.append(profiled_line)
             else:
                 instrumented_lines.append(line)