Skills-4-SE code-instrumentation-generator
Automatically instruments source code to collect runtime information such as function calls, branch decisions, variable values, and execution traces while preserving original program semantics. Use when users need to: (1) Add logging or tracing to code for debugging, (2) Collect runtime execution data for analysis, (3) Monitor function calls and control flow, (4) Track variable values during execution, (5) Generate execution traces for testing or profiling. Supports Python, Java, JavaScript, and C/C++ with configurable instrumentation levels.
git clone https://github.com/ArabelaTso/Skills-4-SE
T=$(mktemp -d) && git clone --depth=1 https://github.com/ArabelaTso/Skills-4-SE "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/code-instrumentation-generator" ~/.claude/skills/arabelatso-skills-4-se-code-instrumentation-generator && rm -rf "$T"
skills/code-instrumentation-generator/SKILL.mdCode Instrumentation Generator
Automatically instrument source code to collect runtime information while preserving program semantics.
Workflow
Follow these steps to instrument code:
1. Analyze the Source Code
Understand the code structure and identify instrumentation points:
- Language detection: Identify the programming language
- Code structure: Parse functions, classes, branches, loops
- Entry/exit points: Locate function boundaries
- Control flow: Identify branches (if/else, switch, loops)
- Variable scope: Understand variable declarations and usage
2. Determine Instrumentation Strategy
Choose appropriate instrumentation based on requirements:
Instrumentation levels:
- Function-level: Entry/exit of functions with parameters and return values
- Branch-level: Execution of conditional branches (if/else, switch cases)
- Statement-level: Individual statement execution
- Variable-level: Variable assignments and value changes
Configuration options:
- Enable/disable specific instrumentation types
- Filter by function names or file patterns
- Set verbosity level
- Choose output format (logs, JSON, CSV)
3. Insert Instrumentation Code
Add instrumentation hooks at identified points:
Function instrumentation:
- Insert entry hook at function start
- Capture function name, parameters, timestamp
- Insert exit hook before returns
- Capture return value, execution time
Branch instrumentation:
- Insert hooks at branch conditions
- Record which branch was taken
- Track branch coverage
Variable instrumentation:
- Insert hooks after variable assignments
- Capture variable name and value
- Track value changes over time
4. Ensure Semantic Preservation
Verify that instrumentation doesn't change program behavior:
- No side effects: Instrumentation code doesn't modify program state
- Exception safety: Instrumentation handles exceptions properly
- Performance: Minimal overhead added
- Thread safety: Instrumentation is safe in concurrent code
5. Generate Output
Provide instrumented code and documentation:
- Instrumented source code: Modified code with instrumentation
- Probe description: Documentation of inserted instrumentation points
- Configuration file: Settings to enable/disable instrumentation
- Usage instructions: How to run and collect data
Language-Specific Patterns
Python
# Original code def calculate_sum(a, b): result = a + b return result # Instrumented code import logging logging.basicConfig(level=logging.INFO) def calculate_sum(a, b): # Function entry instrumentation logging.info(f"ENTER calculate_sum(a={a}, b={b})") result = a + b # Variable instrumentation logging.info(f"VAR result={result}") # Function exit instrumentation logging.info(f"EXIT calculate_sum() -> {result}") return result
Java
// Original code public int calculateSum(int a, int b) { int result = a + b; return result; } // Instrumented code public int calculateSum(int a, int b) { // Function entry instrumentation System.out.println("ENTER calculateSum(a=" + a + ", b=" + b + ")"); int result = a + b; // Variable instrumentation System.out.println("VAR result=" + result); // Function exit instrumentation System.out.println("EXIT calculateSum() -> " + result); return result; }
JavaScript
// Original code function calculateSum(a, b) { const result = a + b; return result; } // Instrumented code function calculateSum(a, b) { // Function entry instrumentation console.log(`ENTER calculateSum(a=${a}, b=${b})`); const result = a + b; // Variable instrumentation console.log(`VAR result=${result}`); // Function exit instrumentation console.log(`EXIT calculateSum() -> ${result}`); return result; }
C/C++
// Original code int calculate_sum(int a, int b) { int result = a + b; return result; } // Instrumented code #include <stdio.h> int calculate_sum(int a, int b) { // Function entry instrumentation printf("ENTER calculate_sum(a=%d, b=%d)\n", a, b); int result = a + b; // Variable instrumentation printf("VAR result=%d\n", result); // Function exit instrumentation printf("EXIT calculate_sum() -> %d\n", result); return result; }
Branch Instrumentation Example
# Original code def check_value(x): if x > 0: return "positive" else: return "non-positive" # Instrumented code def check_value(x): logging.info(f"ENTER check_value(x={x})") # Branch instrumentation if x > 0: logging.info("BRANCH if(x > 0) -> TRUE") result = "positive" else: logging.info("BRANCH if(x > 0) -> FALSE") result = "non-positive" logging.info(f"EXIT check_value() -> {result}") return result
Configuration-Based Instrumentation
Generate a configuration file to control instrumentation:
# instrumentation_config.py INSTRUMENTATION_ENABLED = True INSTRUMENT_FUNCTIONS = True INSTRUMENT_BRANCHES = True INSTRUMENT_VARIABLES = False LOG_LEVEL = "INFO" OUTPUT_FORMAT = "text" # or "json", "csv" # Instrumented code with configuration import instrumentation_config as config def calculate_sum(a, b): if config.INSTRUMENT_FUNCTIONS: logging.info(f"ENTER calculate_sum(a={a}, b={b})") result = a + b if config.INSTRUMENT_VARIABLES: logging.info(f"VAR result={result}") if config.INSTRUMENT_FUNCTIONS: logging.info(f"EXIT calculate_sum() -> {result}") return result
Output Format
Probe Description Document
## Instrumentation Report **File**: calculator.py **Instrumentation Date**: 2024-02-17 **Configuration**: Function-level + Branch-level ### Instrumented Functions 1. **calculate_sum(a, b)** - Entry probe: Line 3 - Exit probe: Line 8 - Captures: Parameters (a, b), return value 2. **check_value(x)** - Entry probe: Line 11 - Branch probe: Line 14 (if x > 0) - Exit probe: Line 19 - Captures: Parameter (x), branch decision, return value ### Instrumentation Statistics - Total functions instrumented: 2 - Total branches instrumented: 1 - Total variables instrumented: 0 - Estimated overhead: <5% ### Usage Run the instrumented code normally. Instrumentation output will be written to: - Console (stdout) - Log file: instrumentation.log (if configured)
Best Practices
- Minimize overhead: Only instrument what's necessary
- Use conditional compilation: Allow disabling instrumentation in production
- Handle exceptions: Ensure instrumentation doesn't crash the program
- Preserve semantics: Never modify program logic
- Thread-safe logging: Use thread-safe logging mechanisms
- Structured output: Use consistent format for easy parsing
- Timestamp everything: Include timestamps for temporal analysis
Advanced Features
Selective Instrumentation
# Only instrument specific functions INSTRUMENTED_FUNCTIONS = ["calculate_sum", "process_data"] def should_instrument(func_name): return func_name in INSTRUMENTED_FUNCTIONS # Apply instrumentation conditionally if should_instrument("calculate_sum"): # Add instrumentation pass
Performance Monitoring
import time def calculate_sum(a, b): start_time = time.time() logging.info(f"ENTER calculate_sum(a={a}, b={b})") result = a + b elapsed = time.time() - start_time logging.info(f"EXIT calculate_sum() -> {result} [time={elapsed:.6f}s]") return result
JSON Output Format
import json import time def calculate_sum(a, b): entry_event = { "type": "function_entry", "function": "calculate_sum", "params": {"a": a, "b": b}, "timestamp": time.time() } print(json.dumps(entry_event)) result = a + b exit_event = { "type": "function_exit", "function": "calculate_sum", "return_value": result, "timestamp": time.time() } print(json.dumps(exit_event)) return result
Constraints
- Preserve semantics: Never change program behavior
- Minimal overhead: Keep instrumentation lightweight
- No side effects: Instrumentation shouldn't modify program state
- Exception safety: Handle errors gracefully
- Configurable: Allow enabling/disabling instrumentation