Memory System A/B Test Design¶

class TimeMetrics:
    execution_time: float          # Total task execution time (seconds)
    time_to_first_action: float   # Time until agent takes first action
    decision_time: float          # Time spent in decision-making
    implementation_time: float    # Time spent writing code

class QualityMetrics:
    test_pass_rate: float         # Percentage of tests passing (0-1)
    code_complexity: int          # Cyclomatic complexity
    error_count: int              # Number of errors during execution
    revision_cycles: int          # Number of code revision iterations
    pylint_score: float           # Automated code quality score (0-10)

class MemoryMetrics:
    memory_retrievals: int        # Number of memory queries
    memory_hits: int              # Number of relevant memories found
    memory_applied: int           # Number of memories actually used
    retrieval_time: float         # Time spent retrieving memories (ms)

class OutputMetrics:
    lines_of_code: int            # LOC generated
    files_modified: int           # Number of files changed
    test_coverage: float          # Test coverage percentage (0-100)
    documentation_completeness: float  # Doc completeness score (0-1)

class DecisionQuality:
    architecture_appropriateness: int    # 1-5 scale
    pattern_selection_quality: int       # 1-5 scale
    error_handling_completeness: int     # 1-5 scale
    edge_case_coverage: int              # 1-5 scale

class PatternRecognition:
    recognized_previous_solution: bool   # Did agent reference past work?
    adapted_pattern_appropriately: bool  # Was adaptation correct?
    avoided_previous_errors: bool        # Prevented repeated mistakes?

# Test harness will collect metrics automatically
class MetricsCollector:
    def __init__(self, scenario_id: str, config: str):
        self.scenario_id = scenario_id
        self.config = config  # "control", "sqlite", or "neo4j"
        self.metrics = {}
        self.start_time = None

    def start_collection(self):
        """Begin metric collection for a test run."""
        self.start_time = time.time()
        self.metrics = {
            "scenario_id": self.scenario_id,
            "config": self.config,
            "timestamp": datetime.now().isoformat(),
            "time": {},
            "quality": {},
            "memory": {},
            "output": {}
        }

    def record_time_metric(self, name: str, value: float):
        """Record a time-based metric."""
        self.metrics["time"][name] = value

    def record_quality_metric(self, name: str, value: Union[int, float]):
        """Record a quality metric."""
        self.metrics["quality"][name] = value

    def record_memory_metric(self, name: str, value: Union[int, float]):
        """Record a memory usage metric."""
        self.metrics["memory"][name] = value

    def record_output_metric(self, name: str, value: Union[int, float]):
        """Record an output metric."""
        self.metrics["output"][name] = value

    def finalize(self) -> dict:
        """Finalize and return collected metrics."""
        self.metrics["time"]["total_execution"] = time.time() - self.start_time
        return self.metrics

# Using standard power analysis
from scipy.stats import power
from statsmodels.stats.power import tt_ind_solve_power

# Expected effect size: 20% improvement
# Cohen's d ≈ 0.5 (medium effect)
# Power = 0.80
# Alpha = 0.05

sample_size = tt_ind_solve_power(
    effect_size=0.5,
    power=0.80,
    alpha=0.05,
    ratio=1.0,
    alternative='two-sided'
)

# Result: ~64 observations per group
# We'll use: 10 scenarios × 5 iterations = 50 per group
# This gives ~75% power (acceptable for initial testing)

from scipy.stats import ttest_rel

def compare_configurations(baseline_times, treatment_times):
    """
    Compare execution times between configurations.

    Uses paired t-test because same scenarios run with different configs.
    """
    # Paired t-test (same scenarios, different conditions)
    t_statistic, p_value = ttest_rel(baseline_times, treatment_times)

    # Calculate effect size (Cohen's d for paired data)
    diff = np.array(treatment_times) - np.array(baseline_times)
    effect_size = np.mean(diff) / np.std(diff)

    # Calculate confidence interval
    conf_interval = stats.t.interval(
        0.95,
        len(diff) - 1,
        loc=np.mean(diff),
        scale=stats.sem(diff)
    )

    return {
        "t_statistic": t_statistic,
        "p_value": p_value,
        "effect_size": effect_size,
        "mean_diff": np.mean(diff),
        "conf_interval_95": conf_interval,
        "significant": p_value < 0.05
    }

from statsmodels.stats.multitest import multipletests

def analyze_all_metrics(baseline_data, treatment_data, metrics):
    """
    Analyze multiple metrics with Bonferroni correction.

    Prevents false positives from multiple testing.
    """
    results = {}
    p_values = []

    for metric in metrics:
        baseline_values = [run[metric] for run in baseline_data]
        treatment_values = [run[metric] for run in treatment_data]

        result = compare_configurations(baseline_values, treatment_values)
        results[metric] = result
        p_values.append(result["p_value"])

    # Apply Bonferroni correction
    corrected = multipletests(p_values, alpha=0.05, method='bonferroni')

    for i, metric in enumerate(metrics):
        results[metric]["corrected_p_value"] = corrected[1][i]
        results[metric]["significant_corrected"] = corrected[0][i]

    return results

def calculate_confidence_intervals(data, confidence=0.95):
    """Calculate confidence intervals for metrics."""
    results = {}

    for metric_name, values in data.items():
        mean = np.mean(values)
        sem = stats.sem(values)
        ci = stats.t.interval(
            confidence,
            len(values) - 1,
            loc=mean,
            scale=sem
        )

        results[metric_name] = {
            "mean": mean,
            "std": np.std(values),
            "ci_lower": ci[0],
            "ci_upper": ci[1],
            "ci_width": ci[1] - ci[0]
        }

    return results

import matplotlib.pyplot as plt
import seaborn as sns

def plot_comparison(baseline, sqlite, neo4j, metric_name):
    """Generate box plot comparison."""
    data = pd.DataFrame({
        'Control (No Memory)': baseline,
        'SQLite Memory': sqlite,
        'Neo4j Memory': neo4j
    })

    plt.figure(figsize=(10, 6))
    sns.boxplot(data=data)
    plt.ylabel(metric_name)
    plt.title(f'{metric_name} Comparison Across Configurations')
    plt.savefig(f'comparison_{metric_name}.png')
    plt.close()

class ControlConfiguration:
    """Configuration with no memory system."""

    def __init__(self):
        self.memory_enabled = False
        self.agents = load_agent_definitions()  # Standard agents

    def run_scenario(self, scenario):
        """Run scenario without memory."""
        # No memory retrieval
        # No memory storage
        # Pure agent execution
        return run_agent_task(scenario, memory=None)

# scripts/memory_test_harness.py

class MemoryTestHarness:
    """Automated A/B test harness for memory systems."""

    def __init__(self, output_dir: str = "test_results"):
        self.output_dir = Path(output_dir)
        self.scenarios = self.load_scenarios()
        self.configurations = {
            "control": ControlConfiguration(),
            "sqlite": SQLiteConfiguration(),
            "neo4j": Neo4jConfiguration()
        }

    def run_full_test_suite(self):
        """Run complete A/B test suite."""
        print("=" * 70)
        print("MEMORY SYSTEM A/B TEST SUITE")
        print("=" * 70)

        # Phase 1: Baseline
        print("\n[Phase 1] Establishing baseline (no memory)...")
        baseline_results = self.run_configuration("control")
        self.save_results(baseline_results, "baseline")

        # Phase 2: SQLite
        print("\n[Phase 2] Testing SQLite memory...")
        sqlite_results = self.run_configuration("sqlite")
        self.save_results(sqlite_results, "sqlite")

        # Analyze Phase 2
        comparison_2 = self.compare_configurations(baseline_results, sqlite_results)
        self.save_comparison(comparison_2, "baseline_vs_sqlite")

        if comparison_2["proceed_recommendation"]:
            # Phase 3: Neo4j (only if Phase 2 successful)
            print("\n[Phase 3] Testing Neo4j memory...")
            neo4j_results = self.run_configuration("neo4j")
            self.save_results(neo4j_results, "neo4j")

            # Analyze Phase 3
            comparison_3 = self.compare_configurations(sqlite_results, neo4j_results)
            self.save_comparison(comparison_3, "sqlite_vs_neo4j")

        # Phase 4: Final Analysis
        print("\n[Phase 4] Generating final report...")
        self.generate_final_report()

    def run_configuration(self, config_name: str) -> List[dict]:
        """Run all scenarios for a configuration."""
        config = self.configurations[config_name]
        results = []

        for scenario in self.scenarios:
            print(f"  Running scenario: {scenario.name}")

            for iteration in range(5):  # 5 iterations per scenario
                print(f"    Iteration {iteration + 1}/5...", end="")

                # Run scenario
                result = self.run_single_test(config, scenario, iteration)
                results.append(result)

                print(f" Done ({result['time']['total_execution']:.1f}s)")

        return results

    def run_single_test(
        self,
        config: Configuration,
        scenario: Scenario,
        iteration: int
    ) -> dict:
        """Run a single test iteration."""
        # Initialize metrics collector
        collector = MetricsCollector(scenario.id, config.name)
        collector.start_collection()

        # Run scenario with configuration
        try:
            output = config.run_scenario(scenario)

            # Collect metrics from output
            self.collect_metrics_from_output(collector, output)

        except Exception as e:
            # Record failure
            collector.record_quality_metric("error_occurred", 1)
            collector.record_quality_metric("error_message", str(e))

        # Finalize and return metrics
        return collector.finalize()

    def compare_configurations(
        self,
        baseline: List[dict],
        treatment: List[dict]
    ) -> dict:
        """Compare two configurations statistically."""
        comparison = {}

        # Extract metrics for comparison
        metrics = ["execution_time", "error_count", "quality_score"]

        for metric in metrics:
            baseline_values = [r["time"]["execution_time"] if metric == "execution_time"
                              else r["quality"][metric] for r in baseline]
            treatment_values = [r["time"]["execution_time"] if metric == "execution_time"
                               else r["quality"][metric] for r in treatment]

            # Run statistical test
            comparison[metric] = self.statistical_test(baseline_values, treatment_values)

        # Determine recommendation
        comparison["proceed_recommendation"] = self.should_proceed(comparison)

        return comparison

    def should_proceed(self, comparison: dict) -> bool:
        """Determine if results justify proceeding."""
        # Check if improvement is statistically significant
        significant = comparison["execution_time"]["significant"]

        # Check if effect size is meaningful
        effect_size = abs(comparison["execution_time"]["effect_size"])
        meaningful = effect_size > 0.5  # Medium effect

        # Check if p-value is strong
        strong = comparison["execution_time"]["p_value"] < 0.01

        return significant and meaningful

    def generate_final_report(self):
        """Generate comprehensive comparison report."""
        report = FinalReportGenerator(self.output_dir)
        report.generate()

        print(f"\n{'=' * 70}")
        print(f"Final report saved to: {self.output_dir}/COMPARISON_RESULTS.md")
        print(f"{'=' * 70}")