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# main.py
import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '.')))
import time
import json
import argparse
from src.agents.recorder_agent import WebAgent
from src.agents.crawler_agent import CrawlerAgent
from src.llm.llm_client import LLMClient
from src.execution.executor import TestExecutor
from src.utils.utils import load_api_key, load_api_version, load_api_base_url, load_llm_model
from src.agents.auth_agent import record_selectors_and_save_auth_state
from src.security.utils import save_report
from src.security.semgrep_scanner import run_semgrep
import logging
import warnings
if __name__ == "__main__":
# Configure logging (DEBUG for detailed logs, INFO for less)
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
# logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
# Suppress noisy logs from specific libraries if needed
logging.getLogger("urllib3").setLevel(logging.WARNING)
logging.getLogger("playwright").setLevel(logging.INFO) # Show Playwright info but not debug
logger = logging.getLogger(__name__) # Logger for main script
# --- Argument Parser ---
parser = argparse.ArgumentParser(description="AI Web Testing Agent - Recorder & Executor")
parser.add_argument(
'--mode',
choices=['record', 'execute','auth' ,'discover', 'security'],
required=True,
help="Mode to run the agent in: 'record' (interactive AI-assisted recording) or 'execute' (deterministic playback)."
)
parser.add_argument(
'--file',
type=str,
help="Path to the JSON test file (required for 'execute' mode)."
)
parser.add_argument(
'--headless',
action='store_true', # Makes it a flag, default False
help="Run executor in headless mode (only applies to 'execute'/discover mode)."
)
parser.add_argument(
'--url', # <<< Added URL argument for discover mode
type=str,
help="Starting URL for website crawling/security (required for 'discover' and 'security' mode)."
)
parser.add_argument(
'--max-pages', # <<< Added max pages argument for discover mode
type=int,
default=10,
help="Maximum number of pages to crawl in 'discover' mode (default: 10)."
)
parser.add_argument(
'--automated',
action='store_true', # Use action='store_true' for boolean flags
help="Run recorder in automated mode (AI makes decisions without user prompts). Only applies to 'record' mode." # Clarified help text
)
parser.add_argument(
'--enable-healing',
action='store_true',
help="Enable self-healing during execution ('execute' mode only)."
)
parser.add_argument(
'--healing-mode',
choices=['soft', 'hard'],
default='soft',
help="Self-healing mode: 'soft' (fix selector) or 'hard' (re-record) ('execute' mode only)."
)
parser.add_argument("--code-path", help="Path to the codebase directory for Semgrep scan (optional).")
parser.add_argument("--output-dir", default="results", help="Directory to save scan reports.")
parser.add_argument('--provider', choices=['gemini', 'openai', 'azure'], default='gemini', help="LLM provider (default: gemini). Choose openai for any OpenAI compatible LLMs.")
parser.add_argument("--semgrep-config", default="auto", help="Semgrep config/ruleset (e.g., 'p/ci', 'r/python'). Default is 'auto'.")
parser.add_argument("--semgrep-timeout", type=int, default=600, help="Semgrep scan timeout in seconds.")
args = parser.parse_args()
# Validate arguments based on mode
if args.mode == 'execute':
if not args.file:
parser.error("--file is required when --mode is 'execute'")
if not args.enable_healing and args.healing_mode != 'soft':
logger.warning("--healing-mode is ignored when --enable-healing is not set.")
elif args.mode == 'record':
if args.enable_healing:
logger.warning("--enable-healing and --healing-mode are ignored in 'record' mode.")
elif args.mode == 'discover':
if not args.url:
parser.error("--url is required when --mode is 'discover'")
if args.enable_healing:
logger.warning("--enable-healing and --healing-mode are ignored in 'discover' mode.")
# --- End Argument Parser ---
# --- Security Warning ---
if args.mode == 'record': # Show warning mainly for recording
warnings.warn(
"SECURITY WARNING: You are about to run an AI agent that interacts with the web based on "
"LLM instructions for recording test steps. Ensure the target environment is safe.",
UserWarning
)
print("\n" + "*"*70)
print("!!! AI WEB TESTING AGENT - RECORDER MODE !!!")
print("This agent interacts with websites to record automated tests.")
print(">> Ensure you target the correct environment (e.g., staging).")
print(">> Avoid recording actions involving highly sensitive production data.")
print(">> You will be prompted to confirm or override AI suggestions.")
print("Proceed with caution.")
print("*"*70 + "\n")
# --- End Security Warning ---
try:
# --- Configuration ---
api_key = load_api_key()
endpoint = load_api_base_url()
api_version = load_api_version()
model_name = load_llm_model()
if not os.path.exists("output"):
try:
os.makedirs("output")
logger.info("Created 'output' directory for screenshots and evidence.")
except OSError as e:
logger.warning(f"Could not create 'output' directory: {e}. Saving evidence/screenshots might fail.")
if args.mode == 'record':
logger.info("Starting in RECORD mode...")
HEADLESS_BROWSER = False # Recording MUST be non-headless
MAX_TEST_ITERATIONS = 50 # Allow more steps for recording complex flows
MAX_HISTORY_FOR_LLM = 10
MAX_STEP_RETRIES = 1 # Retries during recording are for AI suggestion refinement
print("Running in interactive RECORD mode (Browser window is required).")
# --- Initialize Components ---
llm_client = LLMClient(provider=args.provider)
automated = False
if args.automated == True:
automated = True
recorder_agent = WebAgent(
llm_client=llm_client,
headless=HEADLESS_BROWSER, # Must be False
max_iterations=MAX_TEST_ITERATIONS,
max_history_length=MAX_HISTORY_FOR_LLM,
max_retries_per_subtask=MAX_STEP_RETRIES,
is_recorder_mode=True, # Add a flag to agent
automated_mode=automated
)
# --- Get Feature Description ---
print("\nEnter the feature or user flow you want to test.")
print("Examples:")
print("- go to https://practicetestautomation.com/practice-test-login/ and login with username as student and password as Password123 and verify if the login was successful")
print("- Navigate to 'https://example-shop.com', search for 'blue widget', add the first result to the cart, and verify the cart item count increases to 1 (selector: 'span#cart-count').")
print("- On 'https://form-page.com', fill the 'email' field with 'test@example.com', check the 'terms' checkbox (id='terms-cb'), click submit, and verify the success message 'Form submitted!' is shown in 'div.status'.")
feature_description = input("\nPlease enter the test case description: ")
# --- Run the Test ---
if feature_description:
# The run method now handles the recording loop
recording_result = recorder_agent.record(feature_description) # Changed method name
print("\n" + "="*20 + " Recording Result " + "="*20)
if recording_result.get("success"):
print(f"Status: SUCCESS")
print(f"Recording saved to: {recording_result.get('output_file')}")
print(f"Total steps recorded: {recording_result.get('steps_recorded')}")
else:
print(f"Status: FAILED or ABORTED")
print(f"Message: {recording_result.get('message')}")
print("="*58)
else:
print("No test case description entered. Exiting.")
elif args.mode == 'execute':
logger.info(f"Starting in EXECUTE mode for file: {args.file}")
HEADLESS_BROWSER = args.headless # Use flag for executor headless
PIXEL_MISMATCH_THRESHOLD = 0.01
heal_msg = f"Self-Healing: {'ENABLED (' + args.healing_mode + ' mode)' if args.enable_healing else 'DISABLED'}"
print(f"Running in EXECUTE mode ({'Headless' if args.headless else 'Visible Browser'}). {heal_msg}")
llm_client = LLMClient(provider=args.provider)
# Executor doesn't need LLM client directly
executor = TestExecutor(
llm_client=llm_client, # Pass the initialized client
headless=args.headless,
enable_healing=args.enable_healing,
healing_mode=args.healing_mode,
pixel_threshold=PIXEL_MISMATCH_THRESHOLD,
get_performance=True,
get_network_requests=True
# healing_retries can be added as arg if needed
)
test_result = executor.run_test(args.file)
# --- Display Test Execution Results ---
print("\n" + "="*20 + " Execution Result " + "="*20)
print(f"Test File: {test_result.get('test_file', 'N/A')}")
print(f"Status: {test_result.get('status', 'UNKNOWN')}")
print(f"Duration: {test_result.get('duration_seconds', 'N/A')} seconds")
print(f"Message: {test_result.get('message', 'N/A')}")
print(f"Healing: {'ENABLED ('+test_result.get('healing_mode','N/A')+' mode)' if test_result.get('healing_enabled') else 'DISABLED'}")
perf_timing = test_result.get("performance_timing")
if perf_timing:
try:
nav_start = perf_timing.get('navigationStart', 0)
load_end = perf_timing.get('loadEventEnd', 0)
dom_content_loaded = perf_timing.get('domContentLoadedEventEnd', 0)
dom_interactive = perf_timing.get('domInteractive', 0)
if nav_start > 0: # Ensure navigationStart is valid
print("\n--- Performance Metrics (Initial Load) ---")
if load_end > nav_start: print(f" Page Load Time (loadEventEnd): {(load_end - nav_start):,}ms")
if dom_content_loaded > nav_start: print(f" DOM Content Loaded (domContentLoadedEventEnd): {(dom_content_loaded - nav_start):,}ms")
if dom_interactive > nav_start: print(f" DOM Interactive: {(dom_interactive - nav_start):,}ms")
print("-" * 20)
else:
print("\n--- Performance Metrics (Initial Load): navigationStart not captured ---")
except Exception as perf_err:
logger.warning(f"Could not process performance timing: {perf_err}")
print("\n--- Performance Metrics: Error processing data ---")
# ------------------------------------
# --- Network Request Summary ---
network_reqs = test_result.get("network_requests", [])
if network_reqs:
print("\n--- Network Summary ---")
total_reqs = len(network_reqs)
http_error_reqs = len([r for r in network_reqs if (r.get('status', 0) or 0) >= 400])
error_reqs = len([r for r in network_reqs if (r.get('status', 0) or 0) >= 400])
slow_reqs = len([r for r in network_reqs if (r.get('duration_ms') or 0) > 1500]) # Example: > 1.5s
print(f" Total Requests: {total_reqs}")
if http_error_reqs > 0: print(f" Requests >= 400 Status: {http_error_reqs}")
if error_reqs > 0: print(f" Requests >= 400 Status: {error_reqs}")
if slow_reqs > 0: print(f" Requests > 1500ms: {slow_reqs}")
print("(See JSON report for full network details)")
print("-" * 20)
visual_results = test_result.get("visual_assertion_results", [])
if visual_results:
print("\n--- Visual Assertion Results ---")
for vr in visual_results:
status = vr.get('status', 'UNKNOWN')
override = " (LLM Override)" if vr.get('llm_override') else ""
diff_percent = vr.get('pixel_difference_ratio', 0) * 100
thresh_percent = vr.get('pixel_threshold', PIXEL_MISMATCH_THRESHOLD) * 100 # Use executor's default if needed
print(f"- Step {vr.get('step_id')}, Baseline '{vr.get('baseline_id')}': {status}{override}")
print(f" Pixel Difference: {diff_percent:.4f}% (Threshold: {thresh_percent:.2f}%)")
if status == 'FAIL':
if vr.get('diff_image_path'):
print(f" Diff Image: {vr.get('diff_image_path')}")
if vr.get('llm_reasoning'):
print(f" LLM Reasoning: {vr.get('llm_reasoning')}")
elif vr.get('llm_override'): # Passed due to LLM
if vr.get('llm_reasoning'):
print(f" LLM Reasoning: {vr.get('llm_reasoning')}")
print("-" * 20)
# Display Healing Attempts Log
healing_attempts = test_result.get("healing_attempts", [])
if healing_attempts:
print("\n--- Healing Attempts ---")
for attempt in healing_attempts:
outcome = "SUCCESS" if attempt.get('success') else "FAIL"
mode = attempt.get('mode', 'N/A')
print(f"- Step {attempt.get('step_id')}: Attempt {attempt.get('attempt')} ({mode} mode) - {outcome}")
if outcome == "SUCCESS" and mode == "soft":
print(f" Old Selector: {attempt.get('failed_selector')}")
print(f" New Selector: {attempt.get('new_selector')}")
print(f" Reasoning: {attempt.get('reasoning', 'N/A')[:100]}...")
elif outcome == "FAIL" and mode == "soft":
print(f" Failed Selector: {attempt.get('failed_selector')}")
print(f" Reasoning: {attempt.get('reasoning', 'N/A')[:100]}...")
elif mode == "hard":
print(f" Triggered re-recording due to error: {attempt.get('error', 'N/A')[:100]}...")
print("-" * 20)
if test_result.get('status') == 'FAIL':
print("-" * 15 + " Failure Details " + "-" * 15)
failed_step_info = test_result.get('failed_step', {})
print(f"Failed Step ID: {failed_step_info.get('step_id', 'N/A')}")
print(f"Failed Step Description: {failed_step_info.get('description', 'N/A')}")
print(f"Action: {failed_step_info.get('action', 'N/A')}")
# Show the *last* selector tried if healing was attempted
last_selector_tried = failed_step_info.get('selector') # Default to original
last_failed_healing_attempt = next((a for a in reversed(healing_attempts) if a.get('step_id') == failed_step_info.get('step_id') and not a.get('success')), None)
if last_failed_healing_attempt:
last_selector_tried = last_failed_healing_attempt.get('failed_selector')
print(f"Selector Used (Last Attempt): {last_selector_tried or 'N/A'}")
print(f"Error: {test_result.get('error_details', 'N/A')}")
if test_result.get('screenshot_on_failure'):
print(f"Failure Screenshot: {test_result.get('screenshot_on_failure')}")
# (Console message display remains the same)
console_msgs = test_result.get("console_messages_on_failure", [])
if console_msgs:
print("\n--- Console Errors/Warnings (Recent): ---")
for msg in console_msgs:
msg_text = str(msg.get('text',''))
print(f"- [{msg.get('type','UNKNOWN').upper()}] {msg_text[:250]}{'...' if len(msg_text) > 250 else ''}")
total_err_warn = len([m for m in test_result.get("all_console_messages", []) if m.get('type') in ['error', 'warning']])
if total_err_warn > len(console_msgs):
print(f"... (Showing last {len(console_msgs)} of {total_err_warn} total errors/warnings. See JSON report for full logs)")
else:
print("\n--- No relevant console errors/warnings captured on failure. ---")
elif test_result.get('status') == 'PASS':
print(f"Steps Executed: {test_result.get('steps_executed', 'N/A')}")
elif test_result.get('status') == 'HEALING_TRIGGERED':
print(f"\nNOTICE: Hard Healing (re-recording) was triggered.")
print(f"The original execution stopped at Step {test_result.get('failed_step', {}).get('step_id', 'N/A')}.")
print(f"Check logs for the status and output file of the re-recording process.")
print("="*58)
# --- Save Full Execution Results to JSON ---
try:
base_name = os.path.splitext(os.path.basename(args.file))[0]
result_filename = os.path.join("output", f"execution_result_{base_name}_{time.strftime('%Y%m%d_%H%M%S')}.json")
with open(result_filename, 'w', encoding='utf-8') as f:
json.dump(test_result, f, indent=2, ensure_ascii=False)
print(f"\nFull execution result details saved to: {result_filename}")
except Exception as save_err:
logger.error(f"Failed to save full execution result JSON: {save_err}")
elif args.mode == 'discover':
warnings.warn(
"SECURITY WARNING: You are about to run an AI agent that interacts with the web based on "
"LLM instructions or crawling logic. Ensure the target environment is safe.",
UserWarning
)
print("!!! AI WEB TESTING AGENT - DISCOVERY MODE !!!")
print("This agent will crawl the website starting from the provided URL.")
print(">> It will analyze pages and ask an LLM for test step ideas.")
print(">> Ensure you have permission to crawl the target website.")
print(f">> Crawling will be limited to the domain of '{args.url}' and max {args.max_pages} pages.")
print("Proceed with caution.")
print("*"*70 + "\n")
logger.info(f"Starting in DISCOVER mode for URL: {args.url}")
HEADLESS_BROWSER = args.headless # Use the general headless flag
print(f"Running in DISCOVER mode ({'Headless' if HEADLESS_BROWSER else 'Visible Browser'}).")
print(f"Starting URL: {args.url}")
print(f"Max pages to crawl: {args.max_pages}")
# Initialize Components
llm_client = LLMClient(provider=args.provider)
crawler = CrawlerAgent(
llm_client=llm_client,
headless=HEADLESS_BROWSER
)
# Run Discovery
discovery_result = crawler.crawl_and_suggest(args.url, args.max_pages)
# Display Discovery Results
print("\n" + "="*20 + " Discovery Result " + "="*20)
print(f"Status: {'SUCCESS' if discovery_result.get('success') else 'FAILED'}")
print(f"Message: {discovery_result.get('message', 'N/A')}")
print(f"Start URL: {discovery_result.get('start_url', 'N/A')}")
print(f"Base Domain: {discovery_result.get('base_domain', 'N/A')}")
print(f"Pages Visited: {discovery_result.get('pages_visited', 0)}")
discovered_steps_map = discovery_result.get('discovered_steps', {})
print(f"Pages with Suggested Steps: {len(discovered_steps_map)}")
print("-" * 58)
if discovered_steps_map:
print("\n--- Suggested Test Steps per Page ---")
for page_url, steps in discovered_steps_map.items():
print(f"\n[Page: {page_url}]")
if steps:
for i, step_desc in enumerate(steps):
print(f" {i+1}. {step_desc}")
else:
print(" (No specific steps suggested by LLM for this page)")
else:
print("\nNo test step suggestions were generated.")
print("="*58)
# Save Full Discovery Results to JSON
if discovery_result.get('success'): # Only save if crawl succeeded somewhat
try:
# Generate a filename based on the domain
domain = discovery_result.get('base_domain', 'unknown_domain')
# Sanitize domain for filename
safe_domain = "".join(c if c.isalnum() else "_" for c in domain)
result_filename = os.path.join("output", f"discovery_results_{safe_domain}_{time.strftime('%Y%m%d_%H%M%S')}.json")
with open(result_filename, 'w', encoding='utf-8') as f:
json.dump(discovery_result, f, indent=2, ensure_ascii=False)
print(f"\nFull discovery result details saved to: {result_filename}")
except Exception as save_err:
logger.error(f"Failed to save full discovery result JSON: {save_err}")
elif args.mode == 'auth':
# Ensure output directory exists
os.makedirs("output", exist_ok=True)
# --- IMPORTANT: Initialize your LLM Client here ---
# Replace with your actual LLM provider and initialization
try:
# Example using Gemini (replace with your actual setup)
# Ensure GOOGLE_API_KEY is set as an environment variable if using GeminiClient defaults
logger.info(f"Using LLM Provider: {args.provider}")
llm = LLMClient(provider=args.provider)
logger.info("LLM Client initialized.")
except ValueError as e:
logger.error(f"❌ Failed to initialize LLM Client: {e}. Cannot proceed.")
llm = None
except Exception as e:
logger.error(f"❌ An unexpected error occurred initializing LLM Client: {e}. Cannot proceed.", exc_info=True)
llm = None
# ------------------------------------------------
if llm:
success = record_selectors_and_save_auth_state(llm, args.url, args.file)
if success:
print(f"\n--- Authentication state generation completed successfully. ---")
else:
print(f"\n--- Authentication state generation failed. Check logs and screenshots in 'output/'. ---")
else:
print("\n--- Could not initialize LLM Client. Aborting authentication state generation. ---")
elif args.mode == 'security':
logging.info("--- Starting Phase 1: Security Scanning ---")
all_findings = []
# 1. Run ZAP Scan
# logging.info("--- Running ZAP Scan ---")
# if not args.zap_api_key:
# logging.warning("ZAP API key not provided. ZAP scan might fail if API key is required.")
# zap_findings = run_zap_scan(
# target_url=args.url,
# zap_address=args.zap_address,
# zap_api_key=args.zap_api_key,
# spider_timeout=args.zap_spider_timeout,
# scan_timeout=args.zap_scan_timeout
# )
# if zap_findings:
# logging.info(f"Completed ZAP Scan. Found {len(zap_findings)} alerts.")
# all_findings.extend(zap_findings)
# save_report(zap_findings, "zap", args.output_dir, "scan_results")
# else:
# logging.warning("ZAP scan completed with no findings or failed.")
# 2. Run Nuclei Scan
# logging.info("--- Running Nuclei Scan ---")
# nuclei_findings = run_nuclei(
# target_url=args.url,
# templates=args.nuclei_templates,
# output_dir=args.output_dir,
# timeout=args.nuclei_timeout
# )
# if nuclei_findings:
# logging.info(f"Completed Nuclei Scan. Found {len(nuclei_findings)} potential issues.")
# all_findings.extend(nuclei_findings)
# # Nuclei output was already saved by the function, but we can save the parsed list again if needed
# # save_report(nuclei_findings, "nuclei", args.output_dir, "scan_results_parsed")
# else:
# logging.warning("Nuclei scan completed with no findings or failed.")
# 3. Run Semgrep Scan (if code path provided)
# 3. Run Semgrep Scan (if code path provided)
if args.code_path:
logging.info("--- Running Semgrep Scan ---")
semgrep_findings = run_semgrep(
code_path=args.code_path,
config=args.semgrep_config,
output_dir=args.output_dir,
timeout=args.semgrep_timeout
)
if semgrep_findings:
logging.info(f"Completed Semgrep Scan. Found {len(semgrep_findings)} potential issues.")
all_findings.extend(semgrep_findings)
# Semgrep output was already saved, save parsed list if desired
# save_report(semgrep_findings, "semgrep", args.output_dir, "scan_results_parsed")
else:
logging.warning("Semgrep scan completed with no findings or failed.")
else:
logging.info("Skipping Semgrep scan as --code-path was not provided.")
logging.info("--- Phase 1: Security Scanning Complete ---")
logging.info("--- Starting Phase 2: Consolidating Results ---")
logging.info(f"Total findings aggregated from all tools (future): {len(all_findings)}")
# Save the consolidated report
consolidated_report_path = save_report(all_findings, "consolidated", args.output_dir, "consolidated_scan_results")
if consolidated_report_path:
logging.info(f"Consolidated report saved to: {consolidated_report_path}")
print(f"\nConsolidated report saved to: {consolidated_report_path}") # Also print to stdout
else:
logging.error("Failed to save the consolidated report.")
logging.info("--- Phase 2: Consolidation Complete ---")
logging.info("--- Security Automation Script Finished ---")
except ValueError as e:
logger.error(f"Configuration or Input error: {e}")
print(f"Error: {e}")
except ImportError as e:
logger.error(f"Import error: {e}. Make sure requirements are installed and paths correct.")
print(f"Import Error: {e}. Please check installation.")
except Exception as e:
logger.critical(f"An unexpected error occurred in main: {e}", exc_info=True)
print(f"An critical unexpected error occurred: {e}")