directed-test-input-generator

Directed Test Input Generator

Generate test inputs that target specific code paths and hard-to-reach behaviors using program analysis, coverage feedback, and LLM-driven semantic understanding.

Overview

Directed test input generation combines multiple techniques to create test inputs that explore specific execution paths:

1. Path Analysis: Extract control flow paths and their constraints
2. Constraint Solving: Generate inputs satisfying path conditions
3. Coverage Guidance: Use coverage feedback to iteratively reach new paths
4. LLM Semantic Understanding: Leverage code understanding for meaningful inputs

Quick Start

Basic Workflow

# 1. Analyze code to extract paths
from scripts.path_analyzer import analyze_code_paths

paths = analyze_code_paths(source_code)

# 2. Generate inputs for each path
from scripts.input_generator import generate_test_suite

test_suite = generate_test_suite(paths)

# 3. Execute tests and measure coverage
for path_id, test_data in test_suite.items():
    result = execute_test(function, test_data['inputs'])
    verify_coverage(result, test_data['target_line'])

Core Techniques

1. Path Analysis and Extraction

Extract execution paths and their constraints from code:

from scripts.path_analyzer import analyze_code_paths, print_paths

source = """
def validate_age(age, country):
    if age < 0:
        raise ValueError("Invalid age")
    if age < 18:
        return "minor"
    if age >= 65 and country == "US":
        return "senior_us"
    return "adult"
"""

paths = analyze_code_paths(source)
print_paths(paths)

# Output:
# Path #0: exception handler (ValueError) (line 3)
#   Conditions:
#     - age < 0
#
# Path #1: if branch (line 5)
#   Conditions:
#     - age >= 0
#     - age < 18
#
# Path #2: if branch (line 7)
#   Conditions:
#     - age >= 0
#     - age >= 18
#     - age >= 65
#     - country == US

2. Constraint-Based Input Generation

Generate inputs that satisfy specific path constraints: