Continuous Learning in Test Automation: Building Self-Improving Test Systems

class TestFeedbackLoop:
    def __init__(self, model_store, metric_collector):
        self.model_store = model_store
        self.metric_collector = metric_collector
        self.learning_buffer = []

    def execute_test_cycle(self, test_suite):
        """Execute tests and collect feedback"""
        results = []

        for test in test_suite:
            # Execute test
            outcome = test.run()

            # Collect contextual data
            context = {
                'test_id': test.id,
                'execution_time': outcome.duration,
                'failure_reason': outcome.error_message,
                'environment': test.environment,
                'timestamp': outcome.timestamp,
                'flakiness_score': self._calculate_flakiness(test.id)
            }

            # Add to learning buffer
            self.learning_buffer.append({
                'outcome': outcome.status,
                'context': context,
                'test_characteristics': test.get_features()
            })

            results.append(outcome)

        return results

    def learn_from_feedback(self):
        """Process feedback and update models"""
        if len(self.learning_buffer) < 100:
            return  # Wait for sufficient data

        # Extract patterns
        patterns = self._extract_patterns(self.learning_buffer)

        # Update prediction models
        self.model_store.update_models({
            'failure_predictor': patterns['failure_patterns'],
            'flakiness_detector': patterns['flakiness_patterns'],
            'execution_time_estimator': patterns['timing_patterns']
        })

        # Clear processed feedback
        self.learning_buffer.clear()

    def _extract_patterns(self, feedback_data):
        """Extract actionable patterns from feedback"""
        from sklearn.cluster import DBSCAN
        import numpy as np

        # Extract features for clustering
        features = np.array([
            [
                item['context']['execution_time'],
                item['context']['flakiness_score'],
                hash(item['context']['environment']) % 1000
            ]
            for item in feedback_data
        ])

        # Identify failure clusters
        clustering = DBSCAN(eps=0.3, min_samples=5).fit(features)

        return {
            'failure_patterns': clustering.labels_,
            'flakiness_patterns': self._detect_flakiness_patterns(feedback_data),
            'timing_patterns': self._analyze_timing_trends(feedback_data)
        }

from river import tree, metrics, ensemble
import datetime

class OnlineTestOptimizer:
    def __init__(self):
        # Adaptive random forest for failure prediction
        self.failure_model = ensemble.AdaptiveRandomForestClassifier(
            n_models=10,
            max_features='sqrt',
            lambda_value=6
        )

        # Hoeffding tree for test selection
        self.selection_model = tree.HoeffdingTreeClassifier()

        # Performance metrics
        self.failure_metric = metrics.Accuracy()
        self.selection_metric = metrics.Precision()

    def predict_failure_probability(self, test_features):
        """Predict likelihood of test failure"""
        return self.failure_model.predict_proba_one(test_features)

    def update_from_execution(self, test_features, actual_outcome):
        """Learn from single test execution"""
        # Update failure prediction model
        self.failure_model.learn_one(test_features, actual_outcome['failed'])

        # Update metrics
        prediction = self.failure_model.predict_one(test_features)
        self.failure_metric.update(actual_outcome['failed'], prediction)

    def select_tests(self, available_tests, budget):
        """Adaptively select most valuable tests within budget"""
        test_scores = []

        for test in available_tests:
            features = test.extract_features()

            # Predict failure probability
            fail_prob = self.predict_failure_probability(features).get(True, 0.0)

            # Calculate value score
            value_score = self._calculate_test_value(
                fail_probability=fail_prob,
                execution_cost=test.estimated_duration,
                code_coverage=test.coverage_metrics,
                last_execution=test.last_run_timestamp
            )

            test_scores.append((test, value_score))

        # Select highest value tests within budget
        test_scores.sort(key=lambda x: x[1], reverse=True)

        selected = []
        total_cost = 0

        for test, score in test_scores:
            if total_cost + test.estimated_duration <= budget:
                selected.append(test)
                total_cost += test.estimated_duration

        return selected

    def _calculate_test_value(self, fail_probability, execution_cost,
                             code_coverage, last_execution):
        """Calculate value metric for test prioritization"""
        # Time decay factor
        hours_since_execution = (
            datetime.datetime.now() - last_execution
        ).total_seconds() / 3600
        recency_factor = 1 / (1 + hours_since_execution / 24)

        # Value calculation
        value = (
            fail_probability * 0.4 +          # Failure likelihood
            code_coverage * 0.3 +              # Coverage importance
            recency_factor * 0.2 +             # Recency bonus
            (1 / execution_cost) * 0.1         # Efficiency factor
        )

        return value

import re
from collections import defaultdict, Counter
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import AgglomerativeClustering

class FailurePatternLearner:
    def __init__(self):
        self.failure_history = []
        self.pattern_database = defaultdict(list)
        self.vectorizer = TfidfVectorizer(max_features=100)

    def analyze_failure(self, test_failure):
        """Extract and categorize failure patterns"""
        # Parse stack trace
        stack_trace = test_failure.stack_trace
        error_message = test_failure.error_message

        # Extract key elements
        failure_signature = {
            'exception_type': self._extract_exception_type(error_message),
            'failing_component': self._extract_component(stack_trace),
            'error_keywords': self._extract_keywords(error_message),
            'stack_depth': len(stack_trace.split('\n')),
            'timestamp': test_failure.timestamp,
            'environment': test_failure.environment
        }

        # Store failure
        self.failure_history.append({
            'signature': failure_signature,
            'full_context': test_failure
        })

        # Update pattern database
        pattern_key = f"{failure_signature['exception_type']}_{failure_signature['failing_component']}"
        self.pattern_database[pattern_key].append(failure_signature)

        # Check for recurring patterns
        if len(self.pattern_database[pattern_key]) >= 3:
            return self._generate_pattern_alert(pattern_key)

        return None

    def cluster_similar_failures(self, recent_failures=100):
        """Group similar failures using clustering"""
        if len(self.failure_history) < 10:
            return []

        # Get recent failures
        recent = self.failure_history[-recent_failures:]

        # Create text representations
        failure_texts = [
            f"{f['signature']['exception_type']} {' '.join(f['signature']['error_keywords'])}"
            for f in recent
        ]

        # Vectorize
        vectors = self.vectorizer.fit_transform(failure_texts)

        # Cluster
        clustering = AgglomerativeClustering(
            n_clusters=None,
            distance_threshold=0.5,
            linkage='average'
        )
        labels = clustering.fit_predict(vectors.toarray())

        # Group by cluster
        clusters = defaultdict(list)
        for idx, label in enumerate(labels):
            clusters[label].append(recent[idx])

        return clusters

    def suggest_fixes(self, failure_signature):
        """Suggest potential fixes based on historical patterns"""
        # Find similar past failures
        similar_failures = self._find_similar_failures(failure_signature)

        # Extract common resolution patterns
        resolutions = []
        for similar in similar_failures:
            if similar.get('resolution'):
                resolutions.append(similar['resolution'])

        # Rank by frequency
        resolution_counts = Counter(resolutions)

        suggestions = []
        for resolution, count in resolution_counts.most_common(3):
            confidence = count / len(similar_failures)
            suggestions.append({
                'action': resolution,
                'confidence': confidence,
                'evidence_count': count
            })

        return suggestions

    def _extract_exception_type(self, error_message):
        """Extract exception type from error message"""
        match = re.search(r'(\w+Exception|\w+Error)', error_message)
        return match.group(1) if match else 'UnknownException'

    def _extract_component(self, stack_trace):
        """Identify failing component from stack trace"""
        lines = stack_trace.split('\n')
        for line in lines:
            if 'at ' in line and 'test' not in line.lower():
                match = re.search(r'at ([\w.]+)', line)
                if match:
                    return match.group(1).split('.')[0]
        return 'UnknownComponent'

    def _extract_keywords(self, error_message):
        """Extract significant keywords from error message"""
        # Remove common words
        stop_words = {'the', 'a', 'an', 'in', 'to', 'of', 'at', 'for'}
        words = re.findall(r'\b[a-z]{3,}\b', error_message.lower())
        return [w for w in words if w not in stop_words][:5]

from selenium.webdriver.common.by import By
from selenium.common.exceptions import NoSuchElementException
import Levenshtein

class SelfHealingLocator:
    def __init__(self, driver, learning_rate=0.1):
        self.driver = driver
        self.locator_history = {}
        self.learning_rate = learning_rate

    def find_element(self, locator_strategies, element_context):
        """Try multiple strategies and learn from successes"""
        element_id = element_context['id']

        # Try historical best strategy first
        if element_id in self.locator_history:
            best_strategy = self.locator_history[element_id]['best']
            try:
                element = self._try_locator(best_strategy)
                self._update_success(element_id, best_strategy)
                return element
            except NoSuchElementException:
                self._update_failure(element_id, best_strategy)

        # Try all strategies
        for strategy in locator_strategies:
            try:
                element = self._try_locator(strategy)

                # Learn from success
                self._learn_successful_strategy(element_id, strategy, element)
                return element

            except NoSuchElementException:
                continue

        # All strategies failed, attempt healing
        return self._attempt_healing(locator_strategies, element_context)

    def _try_locator(self, strategy):
        """Attempt to locate element with given strategy"""
        by_type, value = strategy
        return self.driver.find_element(by_type, value)

    def _attempt_healing(self, failed_strategies, element_context):
        """Attempt to heal locator by finding similar elements"""
        # Get all elements on page
        all_elements = self.driver.find_elements(By.XPATH, '//*')

        # Score elements by similarity to expected context
        candidates = []
        for elem in all_elements:
            score = self._calculate_similarity(elem, element_context)
            if score > 0.7:  # Threshold for consideration
                candidates.append((elem, score))

        if not candidates:
            raise NoSuchElementException(f"Could not heal locator for {element_context['id']}")

        # Return best match
        candidates.sort(key=lambda x: x[1], reverse=True)
        healed_element = candidates[0][0]

        # Learn new locator strategy
        new_strategy = self._generate_locator_from_element(healed_element)
        self._learn_successful_strategy(
            element_context['id'],
            new_strategy,
            healed_element
        )

        return healed_element

    def _calculate_similarity(self, element, context):
        """Calculate similarity score between element and expected context"""
        score = 0.0

        # Text similarity
        if context.get('text'):
            elem_text = element.text.lower()
            expected_text = context['text'].lower()
            text_sim = 1 - (Levenshtein.distance(elem_text, expected_text) /
                           max(len(elem_text), len(expected_text)))
            score += text_sim * 0.4

        # Attribute similarity
        if context.get('attributes'):
            for attr, value in context['attributes'].items():
                elem_value = element.get_attribute(attr)
                if elem_value:
                    attr_sim = 1 - (Levenshtein.distance(elem_value.lower(), value.lower()) /
                                   max(len(elem_value), len(value)))
                    score += attr_sim * 0.3

        # Position similarity
        if context.get('position'):
            elem_location = element.location
            expected_location = context['position']
            position_diff = abs(elem_location['x'] - expected_location['x']) + \
                          abs(elem_location['y'] - expected_location['y'])
            position_sim = 1 / (1 + position_diff / 100)
            score += position_sim * 0.3

        return score

    def _learn_successful_strategy(self, element_id, strategy, element):
        """Update learning model with successful strategy"""
        if element_id not in self.locator_history:
            self.locator_history[element_id] = {
                'strategies': {},
                'best': strategy
            }

        strategies = self.locator_history[element_id]['strategies']

        # Update strategy score
        if strategy not in strategies:
            strategies[strategy] = {'successes': 0, 'failures': 0, 'score': 0.5}

        strategies[strategy]['successes'] += 1
        strategies[strategy]['score'] = (
            strategies[strategy]['successes'] /
            (strategies[strategy]['successes'] + strategies[strategy]['failures'])
        )

        # Update best strategy
        best_score = max(s['score'] for s in strategies.values())
        for strat, data in strategies.items():
            if data['score'] == best_score:
                self.locator_history[element_id]['best'] = strat
                break

import numpy as np
from collections import deque
import random

class RLTestSelector:
    def __init__(self, num_tests, learning_rate=0.1, discount_factor=0.95):
        self.num_tests = num_tests
        self.lr = learning_rate
        self.gamma = discount_factor
        self.epsilon = 1.0  # Exploration rate
        self.epsilon_decay = 0.995
        self.epsilon_min = 0.01

        # Q-table: state -> action -> value
        self.q_table = {}

        # Experience replay
        self.memory = deque(maxlen=2000)

    def get_state(self, test_suite):
        """Generate state representation"""
        state_features = []

        for test in test_suite:
            state_features.extend([
                test.recent_failure_rate,
                test.code_coverage_delta,
                test.execution_time_normalized,
                test.days_since_last_run
            ])

        return tuple(state_features)

    def select_action(self, state, available_tests):
        """Select tests using epsilon-greedy policy"""
        # Exploration
        if random.random() < self.epsilon:
            return random.sample(
                range(len(available_tests)),
                k=min(10, len(available_tests))
            )

        # Exploitation
        if state not in self.q_table:
            self.q_table[state] = np.zeros(len(available_tests))

        q_values = self.q_table[state]
        return np.argsort(q_values)[-10:].tolist()  # Top 10 tests

    def calculate_reward(self, selected_tests, execution_results):
        """Calculate reward based on execution outcomes"""
        reward = 0.0

        for test, result in zip(selected_tests, execution_results):
            if result.failed:
                # High reward for catching failures
                reward += 10.0

                # Bonus for early detection
                if test.execution_order <= 5:
                    reward += 5.0
            else:
                # Small reward for passing tests (validation)
                reward += 0.1

            # Penalty for execution time
            reward -= result.execution_time / 60.0  # Normalize to minutes

        return reward

    def train(self, state, action, reward, next_state):
        """Update Q-values using Q-learning"""
        if state not in self.q_table:
            self.q_table[state] = np.zeros(self.num_tests)
        if next_state not in self.q_table:
            self.q_table[next_state] = np.zeros(self.num_tests)

        # Q-learning update
        for test_idx in action:
            current_q = self.q_table[state][test_idx]
            max_next_q = np.max(self.q_table[next_state])

            new_q = current_q + self.lr * (reward + self.gamma * max_next_q - current_q)
            self.q_table[state][test_idx] = new_q

        # Decay exploration rate
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

    def remember(self, state, action, reward, next_state):
        """Store experience for replay"""
        self.memory.append((state, action, reward, next_state))

    def replay(self, batch_size=32):
        """Train on random batch from memory"""
        if len(self.memory) < batch_size:
            return

        batch = random.sample(self.memory, batch_size)

        for state, action, reward, next_state in batch:
            self.train(state, action, reward, next_state)

from datetime import datetime, timedelta
import hashlib

class ModelRetrainingOrchestrator:
    def __init__(self, models):
        self.models = models
        self.performance_tracker = {}
        self.data_tracker = {}

    def should_retrain(self, model_name):
        """Determine if model needs retraining"""
        triggers = {
            'time_based': self._check_time_trigger(model_name),
            'performance_degradation': self._check_performance_trigger(model_name),
            'data_drift': self._check_data_drift_trigger(model_name),
            'significant_events': self._check_event_trigger(model_name)
        }

        # Retrain if any trigger is active
        return any(triggers.values()), triggers

    def _check_time_trigger(self, model_name):
        """Check if enough time has passed since last training"""
        last_training = self.models[model_name].last_training_time
        time_threshold = timedelta(days=7)  # Retrain weekly

        return datetime.now() - last_training > time_threshold

    def _check_performance_trigger(self, model_name):
        """Check for performance degradation"""
        if model_name not in self.performance_tracker:
            return False

        recent_accuracy = self.performance_tracker[model_name]['recent_accuracy']
        baseline_accuracy = self.performance_tracker[model_name]['baseline_accuracy']

        # Trigger if performance drops by 5%
        return recent_accuracy < baseline_accuracy * 0.95

    def _check_data_drift_trigger(self, model_name):
        """Detect data distribution drift"""
        if model_name not in self.data_tracker:
            return False

        current_distribution = self.data_tracker[model_name]['current']
        training_distribution = self.data_tracker[model_name]['baseline']

        # Calculate KL divergence or similar metric
        drift_score = self._calculate_drift(current_distribution, training_distribution)

        return drift_score > 0.1  # Threshold for significant drift

    def _check_event_trigger(self, model_name):
        """Check for significant events requiring retraining"""
        events = self.models[model_name].recent_events

        significant_events = [
            'major_release',
            'architecture_change',
            'test_suite_expansion'
        ]

        return any(event['type'] in significant_events for event in events)

    def execute_retraining(self, model_name):
        """Perform incremental or full retraining"""
        model = self.models[model_name]

        # Collect training data
        training_data = self._prepare_training_data(model_name)

        # Choose retraining strategy
        if len(training_data) > 10000:
            # Incremental retraining for large datasets
            self._incremental_retrain(model, training_data)
        else:
            # Full retraining for smaller datasets
            self._full_retrain(model, training_data)

        # Update metadata
        model.last_training_time = datetime.now()
        model.training_data_hash = self._hash_data(training_data)

        # Validate new model
        validation_score = self._validate_model(model)

        if validation_score > self.performance_tracker[model_name]['baseline_accuracy']:
            # Deploy new model
            self._deploy_model(model_name, model)
            print(f"Model {model_name} retrained and deployed. New accuracy: {validation_score:.3f}")
        else:
            # Rollback to previous model
            print(f"Model {model_name} retraining failed validation. Keeping previous version.")

    def _calculate_drift(self, current, baseline):
        """Calculate distribution drift using KL divergence"""
        import scipy.stats as stats
        return stats.entropy(current, baseline)

class ProductionTestingSystem:
    """Enterprise-grade self-improving test system"""

    def __init__(self):
        self.feedback_loop = TestFeedbackLoop(model_store, metric_collector)
        self.online_optimizer = OnlineTestOptimizer()
        self.pattern_learner = FailurePatternLearner()
        self.self_healing = SelfHealingLocator(driver)
        self.rl_selector = RLTestSelector(num_tests=500)
        self.retraining_orchestrator = ModelRetrainingOrchestrator(models)

    def run_intelligent_test_cycle(self, time_budget):
        """Execute optimized test cycle with continuous learning"""
        # Get current system state
        state = self._capture_system_state()

        # Select tests using RL
        available_tests = self._get_available_tests()
        selected_indices = self.rl_selector.select_action(state, available_tests)
        selected_tests = [available_tests[i] for i in selected_indices]

        # Execute with self-healing
        results = []
        for test in selected_tests:
            result = test.run_with_healing(self.self_healing)
            results.append(result)

            # Online learning update
            self.online_optimizer.update_from_execution(
                test.extract_features(),
                result
            )

            # Analyze failures
            if result.failed:
                pattern_alert = self.pattern_learner.analyze_failure(result)
                if pattern_alert:
                    self._handle_pattern_alert(pattern_alert)

        # Calculate reward and train RL agent
        reward = self.rl_selector.calculate_reward(selected_tests, results)
        next_state = self._capture_system_state()
        self.rl_selector.train(state, selected_indices, reward, next_state)

        # Feedback loop processing
        self.feedback_loop.learn_from_feedback()

        # Check retraining triggers
        for model_name in self.retraining_orchestrator.models:
            should_retrain, triggers = self.retraining_orchestrator.should_retrain(model_name)
            if should_retrain:
                self.retraining_orchestrator.execute_retraining(model_name)

        return {
            'tests_executed': len(selected_tests),
            'failures_found': sum(1 for r in results if r.failed),
            'time_used': sum(r.execution_time for r in results),
            'efficiency_score': reward / time_budget
        }