/** * Comprehensive TDD Test Suite for CoScientist Research Engine * * Following London School TDD with emphasis on property-based testing, * scientific methodology validation, and research workflow coordination. * * RED-GREEN-REFACTOR CYCLE: * Focus on hypothesis generation, experimental design validation, * statistical analysis coordination, and research reproducibility. */ import { describe, it, expect, beforeEach, afterEach, jest, } from "@jest/globals"; import { EventEmitter } from "events"; import { CoScientistResearch } from "../co-scientist-research.js"; import { MockFactory, TestDataGenerator, MockBuilder, ContractTester, PerformanceTester, PropertyGenerator, } from "./test-utilities.js"; // Mock external dependencies following London School principles jest.mock("../../../utils/logger.js"); jest.mock("../../../ai/research-ai-engine.js"); jest.mock("../../../research/knowledge-base.js"); describe("CoScientistResearch - London School TDD with Property-Based Testing", () => { let coScientist: CoScientistResearch; let mockConfig: any; let mockLogger: jest.Mocked; let mockAIEngine: jest.Mocked; let mockExperimentEngine: jest.Mocked; let mockAnalysisEngine: jest.Mocked; let mockValidationEngine: jest.Mocked; let mockKnowledgeBase: jest.Mocked; let mockPerformanceMonitor: jest.Mocked; let mockBuilder: MockBuilder; beforeEach(() => { // Setup comprehensive mock configuration for research engine mockConfig = { ai: { model: "co-scientist-v3", capabilities: [ { domain: "chemistry", confidence: 0.92, methods: ["synthesis", "analysis", "optimization"], limitations: ["high_energy_reactions", "toxic_compounds"], }, { domain: "biology", confidence: 0.88, methods: ["genomics", "proteomics", "systems_biology"], limitations: ["live_animal_studies", "clinical_trials"], }, { domain: "physics", confidence: 0.85, methods: ["theoretical", "computational", "experimental"], limitations: ["particle_physics", "quantum_computing"], }, ], reasoning: { causalInference: true, statisticalReasoning: true, scientificMethod: true, hypothesisGeneration: true, }, learning: { continuousLearning: true, knowledgeUpdate: false, experienceRetention: true, crossDomainTransfer: true, }, }, experimentation: { design: { powerAnalysis: true, randomization: { method: "stratified", seed: 42, constraints: ["balanced_groups", "temporal_separation"], }, controls: { enabled: true, types: ["negative", "positive", "vehicle"], matching: true, }, blinding: { enabled: true, level: "double", methods: ["coded_samples", "independent_analysis"], }, }, execution: { automation: { enabled: true, platforms: ["opentrons", "hamilton", "tecan"], protocols: ["liquid_handling", "solid_phase_synthesis", "hplc"], safeguards: [ "volume_checks", "contamination_prevention", "error_handling", ], }, dataCollection: { realTime: true, validation: true, anonymization: true, standardization: true, }, qualityControl: { checks: [ { name: "reagent_quality", type: "statistical", parameters: { threshold: 0.95 }, frequency: "per_batch", }, { name: "instrument_calibration", type: "logical", parameters: { tolerance: 0.02 }, frequency: "daily", }, ], thresholds: [ { metric: "purity", warning: 90, critical: 85, action: "flag_sample", }, { metric: "yield", warning: 70, critical: 50, action: "repeat_experiment", }, ], actions: [ { trigger: "purity_below_critical", action: "abort", parameters: { notify_pi: true }, }, { trigger: "yield_below_warning", action: "adjust", parameters: { optimize_conditions: true }, }, ], }, }, safety: { enabled: true, protocols: [ { domain: "chemistry", rules: [ { condition: "temperature > 200C", action: "emergency_cooling", severity: "high", }, { condition: "pressure > 10bar", action: "pressure_release", severity: "critical", }, ], enforcement: "strict", }, ], monitoring: { continuous: true, parameters: ["temperature", "pressure", "pH", "gas_levels"], thresholds: [ { parameter: "temperature", limit: 250, action: "shutdown" }, { parameter: "toxic_gas_ppm", limit: 10, action: "ventilation_increase", }, ], }, emergency: { stopConditions: [ "fire_detected", "toxic_release", "equipment_failure", ], procedures: [ { trigger: "fire_detected", steps: [ "shutdown_heating", "activate_suppression", "evacuate_area", ], timeout: 30000, }, ], contacts: [ { role: "safety_officer", contact: "safety@example.com", priority: 1, }, { role: "principal_investigator", contact: "pi@example.com", priority: 2, }, ], }, }, }, analysis: { statistical: { methods: ["t_test", "anova", "regression", "non_parametric"], significance: 0.05, power: 0.8, corrections: ["bonferroni", "fdr", "holm"], }, machine_learning: { algorithms: [ "random_forest", "svm", "neural_network", "gradient_boosting", ], validation: { crossValidation: { folds: 5, stratified: true, repeats: 3 }, holdout: { trainRatio: 0.7, validationRatio: 0.15, testRatio: 0.15, }, bootstrap: { samples: 1000, confidence: 0.95 }, }, interpretation: { featureImportance: true, shap: true, lime: true, partialDependence: true, }, }, visualization: { interactive: true, formats: ["png", "svg", "pdf", "html"], themes: ["publication", "presentation", "web"], automation: true, }, interpretation: { causalInference: true, effectSize: true, confidence: true, limitations: true, }, }, validation: { reproducibility: { required: true, standards: [ "fair_principles", "open_science", "reproducible_research", ], documentation: { protocol: true, data: true, code: true, environment: true, }, archival: { repositories: ["zenodo", "figshare", "dryad"], metadata: ["dublin_core", "datacite", "bioschemas"], access: "cc_by", }, }, peerReview: { enabled: false, // Disabled for automated testing reviewers: 2, criteria: [ "methodology", "analysis", "interpretation", "reproducibility", ], blind: true, }, metaAnalysis: { enabled: true, databases: ["pubmed", "web_of_science", "scopus"], criteria: { studyTypes: ["rct", "cohort", "case_control"], populations: ["human", "animal_model", "cell_culture"], interventions: ["chemical", "biological", "physical"], outcomes: ["primary", "secondary", "surrogate"], }, }, }, knowledge: { sources: [ { type: "database", name: "chembl", reliability: 0.95, coverage: ["chemistry", "pharmacology"], }, { type: "literature", name: "pubmed", reliability: 0.9, coverage: ["biomedical", "life_sciences"], }, { type: "database", name: "uniprot", reliability: 0.98, coverage: ["proteins", "genomics"], }, ], integration: { ontologies: ["go", "chebi", "mesh"], standards: ["fair", "w3c", "obo"], mapping: true, validation: true, }, updating: { frequency: "weekly", sources: ["api_feeds", "literature_alerts", "database_updates"], validation: true, versioning: true, }, reasoning: { inference: true, consistency: true, uncertainty: true, explanation: true, }, }, }; mockBuilder = new MockBuilder(); // Setup Logger mock mockLogger = mockBuilder .mockFunction("info", jest.fn()) .mockFunction("debug", jest.fn()) .mockFunction("warn", jest.fn()) .mockFunction("error", jest.fn()) .build() as any; // Setup ResearchAIEngine mock mockAIEngine = { initialize: jest.fn().mockResolvedValue(undefined), generateHypotheses: jest .fn() .mockImplementation(async (domain, observations) => [ { ...MockFactory.createResearchHypothesis(), statement: `Hypothesis generated from observations in ${domain}`, significance: 0.85 + Math.random() * 0.1, }, ]), designMethodology: jest .fn() .mockImplementation(async (hypothesis, domain) => ({ design: "experimental", sampling: { method: "stratified_random", size: 120, criteria: { inclusion: [`${domain}_samples`, "quality_assured"], exclusion: ["contaminated", "outliers"], }, }, analysis: { statistical: [ { name: "anova", type: "parametric", assumptions: ["normality", "homogeneity", "independence"], parameters: { alpha: 0.05 }, }, ], significance: 0.05, power: 0.8, corrections: ["bonferroni"], }, validation: { crossValidation: true, holdoutSet: 20, reproducibility: { seed: 42, environment: "controlled", dependencies: [`${domain}_toolkit==2.1.0`], documentation: true, }, }, })), checkConstraints: jest.fn().mockResolvedValue(true), drawConclusions: jest .fn() .mockImplementation(async (hypothesis, results) => [ { statement: `Analysis supports ${hypothesis.statement}`, confidence: 0.82, evidence: [ { type: "statistical", description: "Significant p-value in primary analysis", strength: 0.9, sources: ["experimental_data"], }, ], implications: [ "Further research recommended", "Clinical relevance potential", ], }, ]), on: jest.fn(), emit: jest.fn(), }; // Setup ExperimentEngine mock mockExperimentEngine = { initialize: jest.fn().mockResolvedValue(undefined), validateDesign: jest.fn().mockResolvedValue(undefined), execute: jest .fn() .mockImplementation(async (hypothesis, methodology) => ({ raw: [ { id: "dataset_primary", name: "Primary Experimental Data", type: "experimental", format: "csv", size: 2048576, path: "/data/primary.csv", checksum: "sha256:abc123", }, ], processed: [ { id: "dataset_processed", name: "Cleaned Experimental Data", type: "processed", format: "parquet", size: 1536000, path: "/data/processed.parquet", checksum: "sha256:def456", }, ], quality: { completeness: 0.96, accuracy: 0.94, consistency: 0.91, validity: 0.95, issues: [ { type: "missing_values", severity: "low", description: "4% missing values in secondary variables", location: "columns 15-18", resolution: "imputation_applied", }, ], }, metadata: { collection: { startDate: new Date("2024-01-15"), endDate: new Date("2024-02-28"), method: "automated_platform", instruments: ["hplc_system_1", "mass_spec_orbitrap"], conditions: ["temperature_controlled", "humidity_monitored"], }, processing: { steps: [ { name: "quality_filter", description: "Remove low quality samples", timestamp: new Date("2024-03-01"), parameters: { quality_threshold: 0.9 }, }, ], software: [ { name: "pandas", version: "2.0.0", configuration: {} }, { name: "scipy", version: "1.10.0", configuration: {} }, ], parameters: { normalization: "z_score", filtering: { method: "outlier_removal", parameters: { threshold: 3.0 }, applied: true, }, transformation: { method: "log_transform", parameters: { base: 10 }, applied: true, }, }, }, variables: [ { name: "reaction_yield", type: "continuous", unit: "percentage", range: [0, 100], missing: 2, distribution: { type: "normal", parameters: { mean: 78.5, std: 12.3 }, statistics: { mean: 78.5, median: 79.2, mode: 80.1, standardDeviation: 12.3, variance: 151.29, skewness: -0.15, kurtosis: 0.23, }, }, }, ], }, })), on: jest.fn(), emit: jest.fn(), }; // Setup AnalysisEngine mock mockAnalysisEngine = { initialize: jest.fn().mockResolvedValue(undefined), validatePower: jest.fn().mockResolvedValue(undefined), analyze: jest .fn() .mockImplementation(async (data, hypothesis, methodology) => ({ statistical: { tests: [ { name: "one_way_anova", statistic: 15.67, pValue: 0.0023, confidence: { level: 0.95, lower: 2.1, upper: 8.9 }, significant: true, effectSize: { measure: "eta_squared", value: 0.34, interpretation: "large", }, }, ], models: [ { name: "linear_regression", formula: "yield ~ temperature + concentration", coefficients: [ { variable: "temperature", estimate: 0.45, standardError: 0.12, tValue: 3.75, pValue: 0.001, }, { variable: "concentration", estimate: 1.23, standardError: 0.28, tValue: 4.39, pValue: 0.0003, }, ], fit: { rSquared: 0.67, adjustedRSquared: 0.64, aic: 145.2, bic: 152.8, logLikelihood: -69.6, }, diagnostics: { residuals: { normality: { test: "shapiro_wilk", statistic: 0.96, pValue: 0.15, normal: true, }, homoscedasticity: { test: "breusch_pagan", statistic: 2.31, pValue: 0.13, homoscedastic: true, }, autocorrelation: { test: "durbin_watson", statistic: 1.89, pValue: 0.24, independent: true, }, }, assumptions: { linearity: true, independence: true, normality: true, homoscedasticity: true, }, outliers: [ { index: 47, value: 95.2, leverage: 0.15, residual: 2.8, influence: 0.42, }, ], }, }, ], summary: { hypothesisSupported: true, confidence: 0.87, powerAnalysis: { observedPower: 0.85, requiredSampleSize: 120, effectSize: 0.34, alpha: 0.05, }, recommendations: [ "Increase sample size for higher power", "Consider additional control variables", "Validate findings with independent dataset", ], }, }, ml: { models: [ { name: "random_forest_regressor", algorithm: "ensemble", hyperparameters: { n_estimators: 100, max_depth: 10, random_state: 42, }, training: { duration: 15.4, iterations: 100, convergence: true, finalLoss: 0.23, }, validation: { metrics: [ { name: "r2_score", value: 0.73, confidence: { level: 0.95, lower: 0.68, upper: 0.78 }, }, { name: "rmse", value: 6.8, confidence: { level: 0.95, lower: 6.1, upper: 7.5 }, }, ], confusionMatrix: undefined, roc: undefined, }, }, ], performance: { bestModel: "random_forest_regressor", comparison: [ { model: "random_forest", metric: "r2_score", value: 0.73, rank: 1, }, { model: "linear_regression", metric: "r2_score", value: 0.67, rank: 2, }, ], crossValidation: { folds: 5, mean: 0.71, std: 0.04, scores: [0.69, 0.73, 0.75, 0.68, 0.71], }, }, interpretation: { featureImportance: [ { feature: "temperature", importance: 0.45, rank: 1 }, { feature: "concentration", importance: 0.32, rank: 2 }, { feature: "ph", importance: 0.18, rank: 3 }, ], shap: { global: { features: ["temperature", "concentration"], values: [0.45, 0.32], baseline: 75.2, }, local: [ { instance: 0, features: ["temperature"], values: [2.3], prediction: 82.1, }, ], }, partialDependence: [ { feature: "temperature", values: [20, 30, 40, 50, 60], dependence: [65.2, 72.8, 80.1, 85.4, 87.9], ice: [ { instance: 0, values: [20, 30, 40], curve: [63.1, 71.2, 79.3], }, ], }, ], }, }, causal: { causalGraph: { nodes: [ { id: "temperature", name: "Temperature", type: "treatment" }, { id: "yield", name: "Reaction Yield", type: "outcome" }, { id: "concentration", name: "Concentration", type: "confounder", }, ], edges: [ { source: "temperature", target: "yield", type: "direct", strength: 0.67, }, { source: "concentration", target: "yield", type: "direct", strength: 0.43, }, ], assumptions: [ "no_unmeasured_confounders", "correct_functional_form", ], }, effects: [ { treatment: "temperature", outcome: "yield", effect: 0.45, confidence: { level: 0.95, lower: 0.21, upper: 0.69 }, method: "instrumental_variables", }, ], confounders: [ { variable: "concentration", strength: 0.43, controlled: true, method: "regression_adjustment", }, ], }, visualization: { plots: [ { id: "scatter_temp_yield", type: "scatter", title: "Temperature vs Yield", data: { x: "temperature", y: "yield" }, config: { theme: "publication", interactive: false, annotations: [ { type: "trend_line", content: "Linear fit: R² = 0.67", position: { x: 0.7, y: 0.9 }, }, ], styling: { colors: ["#1f77b4", "#ff7f0e"], fonts: { family: "Arial", size: 12, weight: "normal" }, layout: { margin: { top: 20, right: 30, bottom: 40, left: 50 }, padding: { top: 10, right: 10, bottom: 10, left: 10 }, grid: true, }, }, }, path: "/plots/scatter_temp_yield.png", }, ], dashboard: { id: "analysis_dashboard", title: "Research Analysis Dashboard", widgets: [ { id: "summary_stats", type: "table", title: "Summary Statistics", data: { rows: 10, cols: 5 }, position: { x: 0, y: 0, width: 6, height: 4 }, }, ], layout: { columns: 12, rows: 8, responsive: true }, }, reports: [ { id: "analysis_report", title: "Statistical Analysis Report", format: "pdf", sections: [ { title: "Executive Summary", content: "Temperature significantly affects reaction yield...", figures: ["scatter_temp_yield"], tables: ["summary_stats"], }, ], metadata: { authors: ["AI Researcher"], created: new Date(), version: "1.0", keywords: ["temperature", "yield", "analysis"], }, }, ], }, })), on: jest.fn(), emit: jest.fn(), }; // Setup ValidationEngine mock mockValidationEngine = { initialize: jest.fn().mockResolvedValue(undefined), checkEthics: jest.fn().mockResolvedValue(undefined), validateResults: jest .fn() .mockImplementation(async (results, methodology) => ({ reproducibilityScore: 0.89, validationTests: [ { name: "data_integrity", passed: true, score: 0.95 }, { name: "methodology_compliance", passed: true, score: 0.87 }, { name: "statistical_validity", passed: true, score: 0.91 }, ], recommendations: [ "Archive raw data with metadata", "Publish analysis code repository", "Consider replication by independent lab", ], certifications: ["fair_compliant", "open_science_ready"], })), }; // Setup KnowledgeBase mock mockKnowledgeBase = { initialize: jest.fn().mockResolvedValue(undefined), getDomainKnowledge: jest.fn().mockImplementation(async (domain) => ({ concepts: [`${domain}_fundamentals`, `${domain}_advanced_topics`], relationships: [`${domain}_cause_effect`, `${domain}_correlations`], methods: [`${domain}_standard_methods`, `${domain}_novel_approaches`], limitations: [ `${domain}_known_constraints`, `${domain}_measurement_limits`, ], recentFindings: [ { title: `Recent advances in ${domain}`, authors: ["Expert A", "Expert B"], year: 2024, relevance: 0.89, }, ], })), getDomainConstraints: jest.fn().mockImplementation(async (domain) => ({ ethical: [`${domain}_ethics`, "general_research_ethics"], technical: [ `${domain}_equipment_limits`, `${domain}_measurement_precision`, ], regulatory: [`${domain}_compliance`, "institutional_requirements"], practical: [ "budget_constraints", "time_limitations", "resource_availability", ], })), }; // Setup ResearchPerformanceMonitor mock mockPerformanceMonitor = { start: jest.fn().mockResolvedValue(undefined), getMetrics: jest .fn() .mockResolvedValue(MockFactory.createPerformanceMetrics()), recordHypothesisGeneration: jest.fn(), recordExperimentExecution: jest.fn(), recordAnalysisCompletion: jest.fn(), recordValidationResults: jest.fn(), }; // Mock constructor dependencies jest.mocked(require("../../../utils/logger.js")).Logger = jest .fn() .mockImplementation(() => mockLogger); // Create CoScientistResearch instance coScientist = new CoScientistResearch(mockConfig); // Inject mocks (coScientist as any).aiEngine = mockAIEngine; (coScientist as any).experimentEngine = mockExperimentEngine; (coScientist as any).analysisEngine = mockAnalysisEngine; (coScientist as any).validationEngine = mockValidationEngine; (coScientist as any).knowledgeBase = mockKnowledgeBase; (coScientist as any).performanceMonitor = mockPerformanceMonitor; }); afterEach(() => { jest.clearAllMocks(); mockBuilder.clear(); }); // ==================== INITIALIZATION BEHAVIOR ==================== describe("Research Engine Initialization and Component Coordination", () => { it("should coordinate initialization of all research subsystems", async () => { // ARRANGE const initializeSpy = jest.spyOn(coScientist, "initialize"); // ACT await coScientist.initialize(); // ASSERT - Verify initialization coordination expect(initializeSpy).toHaveBeenCalledTimes(1); expect(mockKnowledgeBase.initialize).toHaveBeenCalled(); expect(mockAIEngine.initialize).toHaveBeenCalled(); expect(mockExperimentEngine.initialize).toHaveBeenCalled(); expect(mockAnalysisEngine.initialize).toHaveBeenCalled(); expect(mockValidationEngine.initialize).toHaveBeenCalled(); expect(mockPerformanceMonitor.start).toHaveBeenCalled(); expect(mockLogger.info).toHaveBeenCalledWith( "Initializing CoScientist Research Engine", ); }); it("should handle component initialization failures with proper error propagation", async () => { // ARRANGE const initError = new Error("Knowledge base initialization failed"); mockKnowledgeBase.initialize.mockRejectedValueOnce(initError); // ACT & ASSERT await expect(coScientist.initialize()).rejects.toThrow( "Knowledge base initialization failed", ); expect(mockLogger.error).toHaveBeenCalledWith( "Failed to initialize research engine", initError, ); }); it("should establish event handler contracts for research coordination", async () => { // ACT await coScientist.initialize(); // ASSERT - Verify event handler setup expect(mockAIEngine.on).toHaveBeenCalledWith( "hypothesis:generated", expect.any(Function), ); expect(mockExperimentEngine.on).toHaveBeenCalledWith( "experiment:completed", expect.any(Function), ); expect(mockAnalysisEngine.on).toHaveBeenCalledWith( "analysis:completed", expect.any(Function), ); }); }); // ==================== HYPOTHESIS GENERATION BEHAVIOR ==================== describe("Hypothesis Generation with AI Coordination", () => { beforeEach(async () => { await coScientist.initialize(); }); it("should coordinate hypothesis generation with domain knowledge integration", async () => { // ARRANGE const domain = "chemistry"; const observations = [ "Reaction yield decreases at higher temperatures", "pH affects catalyst stability", "Solvent polarity influences product selectivity", ]; // ACT const result = await coScientist.generateHypotheses(domain, observations); // ASSERT - Verify coordination expect(result.success).toBe(true); expect(result.data.length).toBeGreaterThan(0); expect(mockKnowledgeBase.getDomainKnowledge).toHaveBeenCalledWith(domain); expect(mockAIEngine.generateHypotheses).toHaveBeenCalledWith( domain, observations, expect.any(Object), // Domain knowledge undefined, // No constraints ); expect(mockLogger.info).toHaveBeenCalledWith( "Generating research hypotheses", expect.objectContaining({ domain, observationsCount: observations.length, }), ); }); it("should validate and rank generated hypotheses by significance", async () => { // ARRANGE const domain = "biology"; const observations = ["Gene expression varies with environmental stress"]; // Mock multiple hypotheses with different significance scores mockAIEngine.generateHypotheses.mockResolvedValueOnce([ { ...MockFactory.createResearchHypothesis(), significance: 0.95 }, { ...MockFactory.createResearchHypothesis(), significance: 0.78 }, { ...MockFactory.createResearchHypothesis(), significance: 0.62 }, ]); // ACT const result = await coScientist.generateHypotheses(domain, observations); // ASSERT expect(result.success).toBe(true); expect(result.data).toHaveLength(3); // Should be sorted by significance (descending) expect(result.data[0].significance).toBeGreaterThan( result.data[1].significance, ); expect(result.data[1].significance).toBeGreaterThan( result.data[2].significance, ); }); // ==================== PROPERTY-BASED TESTING FOR HYPOTHESIS GENERATION ==================== it("should generate valid hypotheses for any valid domain and observations", async () => { // ARRANGE - Property-based test inputs const validInputs = PropertyGenerator.generateTestCases(() => { const domains = [ "chemistry", "biology", "physics", "materials", "environmental", ]; const domain = domains[Math.floor(Math.random() * domains.length)]; const observationCount = Math.floor(Math.random() * 5) + 1; // 1-5 observations const observations = Array.from( { length: observationCount }, () => `Observation about ${domain}: ${TestDataGenerator.randomString(20)}`, ); return { domain, observations }; }, 10); // ACT & ASSERT for (const { domain, observations } of validInputs) { const result = await coScientist.generateHypotheses( domain, observations, ); expect(result.success).toBe(true); expect(result.data.length).toBeGreaterThan(0); expect(mockAIEngine.generateHypotheses).toHaveBeenCalledWith( domain, observations, expect.any(Object), undefined, ); } }); it("should handle constraint-based hypothesis generation", async () => { // ARRANGE const domain = "chemistry"; const observations = ["Temperature affects reaction rate"]; const constraints = { maxComplexity: 3, requireQuantitative: true, excludeHazardous: true, timeLimit: 30, // days }; // ACT const result = await coScientist.generateHypotheses( domain, observations, constraints, ); // ASSERT expect(result.success).toBe(true); expect(mockAIEngine.generateHypotheses).toHaveBeenCalledWith( domain, observations, expect.any(Object), constraints, ); }); }); // ==================== PROJECT CREATION AND METHODOLOGY DESIGN ==================== describe("Research Project Creation with Methodology Coordination", () => { beforeEach(async () => { await coScientist.initialize(); }); it("should coordinate project creation with AI-designed methodology", async () => { // ARRANGE const hypothesis = MockFactory.createResearchHypothesis(); const title = "Temperature Effects on Catalytic Efficiency"; const domain = "chemistry"; // ACT const result = await coScientist.createProject(title, domain, hypothesis); // ASSERT expect(result.success).toBe(true); expect(result.data.title).toBe(title); expect(result.data.domain).toBe(domain); expect(result.data.status).toBe("design"); expect(mockAIEngine.designMethodology).toHaveBeenCalledWith( hypothesis, domain, ); expect(mockLogger.info).toHaveBeenCalledWith( "Creating research project", expect.objectContaining({ title, domain }), ); }); it("should validate project design with ethical and methodological checks", async () => { // ARRANGE const hypothesis = MockFactory.createResearchHypothesis(); const title = "Ethical Validation Test"; const domain = "biology"; // ACT const result = await coScientist.createProject(title, domain, hypothesis); // ASSERT expect(result.success).toBe(true); expect(mockExperimentEngine.validateDesign).toHaveBeenCalled(); expect(mockValidationEngine.checkEthics).toHaveBeenCalledWith( hypothesis, expect.any(Object), // Methodology ); expect(mockAnalysisEngine.validatePower).toHaveBeenCalled(); }); it("should use provided partial methodology and complete it with AI", async () => { // ARRANGE const hypothesis = MockFactory.createResearchHypothesis(); const title = "Partial Methodology Test"; const domain = "physics"; const partialMethodology = { design: "observational", sampling: { method: "convenience", size: 50, }, }; // ACT const result = await coScientist.createProject( title, domain, hypothesis, partialMethodology, ); // ASSERT expect(result.success).toBe(true); expect(result.data.methodology.design).toBe("observational"); expect(result.data.methodology.sampling.method).toBe("convenience"); expect(result.data.methodology.sampling.size).toBe(50); }); // ==================== PROPERTY-BASED TESTING FOR PROJECT PARAMETERS ==================== it("should create valid projects for diverse research configurations", async () => { // ARRANGE - Property-based test cases const validProjectConfigs = PropertyGenerator.generateTestCases(() => { const domains = [ "chemistry", "biology", "physics", "materials", "psychology", ]; const designs = [ "experimental", "observational", "theoretical", "computational", ]; const sampleSizes = [30, 50, 100, 200, 500]; return { title: `Study ${TestDataGenerator.randomString(8)}`, domain: domains[Math.floor(Math.random() * domains.length)], design: designs[Math.floor(Math.random() * designs.length)], sampleSize: sampleSizes[Math.floor(Math.random() * sampleSizes.length)], }; }, 8); // ACT & ASSERT for (const config of validProjectConfigs) { const hypothesis = { ...MockFactory.createResearchHypothesis(), variables: [ { name: "independent_var", type: "independent", dataType: "numerical", measurement: { unit: "units", scale: [0, 100], precision: 0.1, method: "measurement_device", }, }, ], }; const methodology = { design: config.design, sampling: { method: "random", size: config.sampleSize }, }; const result = await coScientist.createProject( config.title, config.domain, hypothesis, methodology, ); expect(result.success).toBe(true); expect(result.data.domain).toBe(config.domain); expect(result.data.methodology.design).toBe(config.design); expect(result.data.methodology.sampling.size).toBe(config.sampleSize); } }); }); // ==================== PROJECT EXECUTION ORCHESTRATION ==================== describe("Research Project Execution Coordination", () => { let projectId: string; beforeEach(async () => { await coScientist.initialize(); const project = await coScientist.createProject( "Execution Test Project", "chemistry", MockFactory.createResearchHypothesis(), ); projectId = project.data!.id; }); it("should coordinate project execution through all research phases", async () => { // ARRANGE const executeSpy = jest.spyOn(coScientist, "executeProject"); // ACT const result = await coScientist.executeProject(projectId); // ASSERT expect(result.success).toBe(true); expect(executeSpy).toHaveBeenCalledWith(projectId); expect(mockLogger.info).toHaveBeenCalledWith( "Executing research project", expect.objectContaining({ projectId }), ); }); it("should coordinate experiment execution with data collection", async () => { // ARRANGE await coScientist.executeProject(projectId); const project = (await coScientist.getProject(projectId)).data!; // ACT - Wait for async execution to complete phases await new Promise((resolve) => setTimeout(resolve, 100)); // ASSERT expect(mockExperimentEngine.execute).toHaveBeenCalledWith( project.hypothesis, project.methodology, ); expect( mockPerformanceMonitor.recordExperimentExecution, ).toHaveBeenCalled(); }); it("should coordinate data analysis with statistical and ML methods", async () => { // ARRANGE await coScientist.executeProject(projectId); // ACT - Wait for async execution to complete await new Promise((resolve) => setTimeout(resolve, 100)); // ASSERT expect(mockAnalysisEngine.analyze).toHaveBeenCalledWith( expect.any(Object), // Experimental data expect.any(Object), // Hypothesis expect.any(Object), // Methodology ); expect( mockPerformanceMonitor.recordAnalysisCompletion, ).toHaveBeenCalled(); }); it("should coordinate AI-powered conclusion generation", async () => { // ARRANGE await coScientist.executeProject(projectId); // ACT - Wait for async execution await new Promise((resolve) => setTimeout(resolve, 100)); // ASSERT expect(mockAIEngine.drawConclusions).toHaveBeenCalledWith( expect.any(Object), // Hypothesis expect.any(Object), // Analysis results ); }); it("should handle project execution failure with proper error management", async () => { // ARRANGE const executionError = new Error("Experiment execution failed"); mockExperimentEngine.execute.mockRejectedValueOnce(executionError); // ACT await coScientist.executeProject(projectId); // Wait for async error handling await new Promise((resolve) => setTimeout(resolve, 100)); // ASSERT const project = (await coScientist.getProject(projectId)).data!; expect(project.status).toBe("failed"); expect(mockLogger.error).toHaveBeenCalledWith( "Project execution failed", expect.objectContaining({ projectId }), ); }); }); // ==================== STATISTICAL ANALYSIS VALIDATION ==================== describe("Statistical Analysis and Validation Coordination", () => { let projectId: string; beforeEach(async () => { await coScientist.initialize(); const project = await coScientist.createProject( "Statistical Test Project", "biology", MockFactory.createResearchHypothesis(), ); projectId = project.data!.id; // Complete project execution await coScientist.executeProject(projectId); await new Promise((resolve) => setTimeout(resolve, 100)); }); it("should coordinate comprehensive statistical validation", async () => { // ACT const result = await coScientist.validateResults(projectId); // ASSERT expect(result.success).toBe(true); expect(result.data.reproducibilityScore).toBeGreaterThan(0.8); expect(result.data.validationTests).toEqual( expect.arrayContaining([ expect.objectContaining({ name: "data_integrity", passed: true }), expect.objectContaining({ name: "methodology_compliance", passed: true, }), expect.objectContaining({ name: "statistical_validity", passed: true, }), ]), ); expect(mockValidationEngine.validateResults).toHaveBeenCalled(); }); it("should provide actionable recommendations for research improvement", async () => { // ACT const result = await coScientist.validateResults(projectId); // ASSERT expect(result.success).toBe(true); expect(result.data.recommendations).toEqual( expect.arrayContaining([ "Archive raw data with metadata", "Publish analysis code repository", "Consider replication by independent lab", ]), ); expect(result.data.certifications).toContain("fair_compliant"); }); // ==================== PROPERTY-BASED TESTING FOR STATISTICAL VALIDITY ==================== it("should validate statistical results across different analysis methods", async () => { // ARRANGE - Property-based test for different statistical approaches const analysisConfigs = PropertyGenerator.generateTestCases(() => { const methods = [ "t_test", "anova", "regression", "non_parametric", "bayesian", ]; const significance = [0.01, 0.05, 0.1]; const power = [0.8, 0.85, 0.9]; return { method: methods[Math.floor(Math.random() * methods.length)], significance: significance[Math.floor(Math.random() * significance.length)], power: power[Math.floor(Math.random() * power.length)], }; }, 6); // ACT & ASSERT for (const config of analysisConfigs) { // Mock analysis engine to return results for different methods mockAnalysisEngine.analyze.mockResolvedValueOnce({ statistical: { tests: [ { name: config.method, statistic: Math.random() * 10, pValue: Math.random() * config.significance, confidence: { level: 0.95, lower: 1.0, upper: 5.0 }, significant: true, }, ], models: [], summary: { hypothesisSupported: true, confidence: 0.85, powerAnalysis: { observedPower: config.power, requiredSampleSize: 100, effectSize: 0.5, alpha: config.significance, }, recommendations: [], }, }, ml: { models: [], performance: {}, interpretation: {} }, causal: { causalGraph: { nodes: [], edges: [], assumptions: [] }, effects: [], confounders: [], }, visualization: { plots: [], dashboard: {}, reports: [] }, }); const validationResult = await coScientist.validateResults(projectId); expect(validationResult.success).toBe(true); expect(validationResult.data.reproducibilityScore).toBeGreaterThan(0.7); } }); }); // ==================== MACHINE LEARNING INTEGRATION ==================== describe("Machine Learning Analysis Integration", () => { beforeEach(async () => { await coScientist.initialize(); }); it("should coordinate ML model training with feature interpretation", async () => { // ARRANGE const project = await coScientist.createProject( "ML Integration Test", "chemistry", MockFactory.createResearchHypothesis(), ); await coScientist.executeProject(project.data!.id); // Wait for execution await new Promise((resolve) => setTimeout(resolve, 100)); // ASSERT expect(mockAnalysisEngine.analyze).toHaveBeenCalled(); // Verify ML analysis was included const analysisCall = mockAnalysisEngine.analyze.mock.calls[0]; expect(analysisCall).toBeDefined(); }); it("should provide interpretable ML results with SHAP values", async () => { // ARRANGE const project = await coScientist.createProject( "ML Interpretation Test", "biology", MockFactory.createResearchHypothesis(), ); const projectId = project.data!.id; await coScientist.executeProject(projectId); await new Promise((resolve) => setTimeout(resolve, 100)); // ACT const projectResult = await coScientist.getProject(projectId); const results = projectResult.data!.results!; // ASSERT expect( results.analysis.ml.interpretation.featureImportance, ).toBeDefined(); expect(results.analysis.ml.interpretation.shap).toBeDefined(); expect( results.analysis.ml.interpretation.partialDependence, ).toBeDefined(); }); // ==================== PROPERTY-BASED TESTING FOR ML CONFIGURATIONS ==================== it("should handle diverse ML algorithm configurations", async () => { // ARRANGE - Property-based testing for ML configurations const mlConfigs = PropertyGenerator.generateTestCases(() => { const algorithms = [ "random_forest", "svm", "neural_network", "gradient_boosting", "linear_regression", ]; const validationMethods = ["cross_validation", "holdout", "bootstrap"]; const interpretationMethods = [ "shap", "lime", "permutation_importance", ]; return { algorithm: algorithms[Math.floor(Math.random() * algorithms.length)], validation: validationMethods[ Math.floor(Math.random() * validationMethods.length) ], interpretation: interpretationMethods[ Math.floor(Math.random() * interpretationMethods.length) ], }; }, 5); // ACT & ASSERT for (const config of mlConfigs) { const project = await coScientist.createProject( `ML Config Test ${config.algorithm}`, "chemistry", MockFactory.createResearchHypothesis(), ); await coScientist.executeProject(project.data!.id); await new Promise((resolve) => setTimeout(resolve, 50)); const projectResult = await coScientist.getProject(project.data!.id); // Should successfully create and execute project regardless of ML configuration expect(projectResult.success).toBe(true); expect(projectResult.data!.status).toBeOneOf([ "execution", "analysis", "validation", "completed", ]); } }); }); // ==================== CAUSAL INFERENCE ANALYSIS ==================== describe("Causal Inference and Reasoning", () => { beforeEach(async () => { await coScientist.initialize(); }); it("should coordinate causal graph construction with domain knowledge", async () => { // ARRANGE const hypothesis = { ...MockFactory.createResearchHypothesis(), statement: "Increased study time causes improved test scores", }; const project = await coScientist.createProject( "Causal Inference Test", "psychology", hypothesis, ); await coScientist.executeProject(project.data!.id); await new Promise((resolve) => setTimeout(resolve, 100)); // ACT const projectResult = await coScientist.getProject(project.data!.id); const causalResults = projectResult.data!.results!.analysis.causal; // ASSERT expect(causalResults.causalGraph).toBeDefined(); expect(causalResults.causalGraph.nodes.length).toBeGreaterThan(0); expect(causalResults.causalGraph.edges.length).toBeGreaterThan(0); expect(causalResults.effects.length).toBeGreaterThan(0); expect(causalResults.confounders).toBeDefined(); }); it("should identify and control for confounding variables", async () => { // ARRANGE const project = await coScientist.createProject( "Confounder Control Test", "epidemiology", MockFactory.createResearchHypothesis(), ); await coScientist.executeProject(project.data!.id); await new Promise((resolve) => setTimeout(resolve, 100)); // ACT const projectResult = await coScientist.getProject(project.data!.id); const confounders = projectResult.data!.results!.analysis.causal.confounders; // ASSERT expect(confounders).toEqual( expect.arrayContaining([ expect.objectContaining({ variable: expect.any(String), strength: expect.any(Number), controlled: true, method: expect.any(String), }), ]), ); }); }); // ==================== PERFORMANCE AND SCALABILITY ==================== describe("Performance Monitoring and Research Scalability", () => { beforeEach(async () => { await coScientist.initialize(); }); it("should monitor research pipeline performance metrics", async () => { // ARRANGE const project = await coScientist.createProject( "Performance Test", "chemistry", MockFactory.createResearchHypothesis(), ); // ACT await coScientist.executeProject(project.data!.id); const metricsResult = await coScientist.getMetrics(); // ASSERT expect(metricsResult.success).toBe(true); ContractTester.validatePerformanceMetrics(metricsResult.data); expect(mockPerformanceMonitor.getMetrics).toHaveBeenCalled(); }); it("should meet performance requirements for research operations", async () => { // ARRANGE & ACT const performanceTest = PerformanceTester.createPerformanceTest( "hypothesis_generation", () => coScientist.generateHypotheses("chemistry", [ "Temperature affects reaction rate", ]), 1000, // 1 second max 3, // 3 iterations ); // ASSERT await performanceTest(); }); it("should handle concurrent research projects efficiently", async () => { // ARRANGE - Create multiple concurrent projects const concurrentProjects = Array.from({ length: 5 }, (_, i) => coScientist.createProject( `Concurrent Project ${i}`, ["chemistry", "biology", "physics"][i % 3], MockFactory.createResearchHypothesis(), ), ); // ACT const projects = await Promise.all(concurrentProjects); const executionPromises = projects .filter((p) => p.success) .map((p) => coScientist.executeProject(p.data!.id)); await Promise.allSettled(executionPromises); // ASSERT const allProjects = await coScientist.listProjects(); expect(allProjects.success).toBe(true); expect(allProjects.data.length).toBe(5); }); }); // ==================== CONTRACT AND ERROR HANDLING ==================== describe("Contract Validation and Error Handling", () => { beforeEach(async () => { await coScientist.initialize(); }); it("should maintain service response contracts for all operations", async () => { // ARRANGE & ACT const hypothesesResult = await coScientist.generateHypotheses( "chemistry", ["Test observation"], ); const projectResult = await coScientist.createProject( "Test", "biology", MockFactory.createResearchHypothesis(), ); const listResult = await coScientist.listProjects(); const metricsResult = await coScientist.getMetrics(); // ASSERT ContractTester.validateServiceResponse(hypothesesResult); ContractTester.validateServiceResponse(projectResult); ContractTester.validateServiceResponse(listResult); ContractTester.validateServiceResponse(metricsResult); }); it("should validate event emitter contract for research monitoring", async () => { // ARRANGE const expectedEvents = [ "hypotheses:generated", "project:created", "project:started", "project:progress", "project:completed", "project:failed", "experiment:completed", "analysis:completed", ]; // ACT & ASSERT ContractTester.validateEventEmitter(coScientist, expectedEvents); }); it("should handle research validation failures gracefully", async () => { // ARRANGE const project = await coScientist.createProject( "Validation Failure Test", "chemistry", MockFactory.createResearchHypothesis(), ); const validationError = new Error("Statistical validation failed"); mockValidationEngine.validateResults.mockRejectedValueOnce( validationError, ); // ACT const result = await coScientist.validateResults(project.data!.id); // ASSERT expect(result.success).toBe(false); expect(result.error?.code).toBe("VALIDATION_FAILED"); expect(mockLogger.error).toHaveBeenCalledWith( "Failed to validate results", expect.objectContaining({ projectId: project.data!.id }), ); }); // ==================== PROPERTY-BASED ERROR TESTING ==================== it("should handle various error scenarios robustly", async () => { // ARRANGE - Property-based error scenarios const errorScenarios = [ { component: "aiEngine", method: "generateHypotheses", error: new Error("AI service timeout"), }, { component: "experimentEngine", method: "execute", error: new Error("Equipment malfunction"), }, { component: "analysisEngine", method: "analyze", error: new Error("Insufficient data"), }, { component: "validationEngine", method: "validateResults", error: new Error("Validation criteria not met"), }, ]; // ACT & ASSERT for (const scenario of errorScenarios) { const mock = (coScientist as any)[scenario.component]; mock[scenario.method].mockRejectedValueOnce(scenario.error); try { if (scenario.method === "generateHypotheses") { await coScientist.generateHypotheses("test", ["test"]); } else { const project = await coScientist.createProject( "Error Test", "chemistry", MockFactory.createResearchHypothesis(), ); await coScientist.executeProject(project.data!.id); if (scenario.method === "validateResults") { await coScientist.validateResults(project.data!.id); } } } catch (error) { // Expected for some operations } expect(mockLogger.error).toHaveBeenCalled(); // Reset mock for next iteration mock[scenario.method].mockReset(); if (scenario.method === "generateHypotheses") { mock[scenario.method].mockResolvedValue([ MockFactory.createResearchHypothesis(), ]); } else { mock[scenario.method].mockResolvedValue({}); } } }); }); // ==================== RESEARCH REPRODUCIBILITY ==================== describe("Research Reproducibility and Open Science", () => { beforeEach(async () => { await coScientist.initialize(); }); it("should generate FAIR-compliant research documentation", async () => { // ARRANGE const project = await coScientist.createProject( "FAIR Compliance Test", "chemistry", MockFactory.createResearchHypothesis(), ); await coScientist.executeProject(project.data!.id); await new Promise((resolve) => setTimeout(resolve, 100)); // ACT const validationResult = await coScientist.validateResults( project.data!.id, ); // ASSERT expect(validationResult.success).toBe(true); expect(validationResult.data.certifications).toContain("fair_compliant"); expect(validationResult.data.recommendations).toEqual( expect.arrayContaining([ "Archive raw data with metadata", "Publish analysis code repository", ]), ); }); it("should support reproducible research workflows", async () => { // ARRANGE const reproducibleHypothesis = { ...MockFactory.createResearchHypothesis(), methodology: { ...MockFactory.createResearchHypothesis().methodology, validation: { crossValidation: true, holdoutSet: 20, reproducibility: { seed: 42, environment: "controlled_lab", dependencies: ["python==3.9", "numpy==1.21.0"], documentation: true, }, }, }, }; // ACT const project = await coScientist.createProject( "Reproducibility Test", "biology", reproducibleHypothesis, ); // ASSERT expect(project.success).toBe(true); expect(project.data.methodology.validation.reproducibility).toBeDefined(); expect(project.data.methodology.validation.reproducibility.seed).toBe(42); }); }); }); /** * RED-GREEN-REFACTOR CYCLE DOCUMENTATION FOR COSCIENTIST RESEARCH: * * This comprehensive test suite demonstrates London School TDD applied to * scientific research automation and validation: * * 1. PROPERTY-BASED TESTING INTEGRATION: * - Tests validate behavior across diverse input spaces * - Hypothesis generation tested with various domains and observations * - Statistical analysis validated across different methods and parameters * - ML configurations tested with multiple algorithms and validation approaches * * 2. SCIENTIFIC METHODOLOGY VALIDATION: * - All aspects of research pipeline tested: hypothesis → experiment → analysis → validation * - Statistical validity, causal inference, and reproducibility requirements * - AI integration for research enhancement and optimization * * 3. COMPLEX RESEARCH WORKFLOW COORDINATION: * - Multi-phase research execution with proper error handling * - Integration of traditional statistics with modern ML approaches * - Knowledge base integration for domain-specific research guidance * * 4. LONDON SCHOOL PRINCIPLES IN SCIENTIFIC CONTEXT: * - RED: Define expected scientific workflow behavior through failing tests * - GREEN: Implement research coordination logic to satisfy scientific contracts * - REFACTOR: Improve research workflow patterns while maintaining scientific rigor * * Key Scientific Collaboration Patterns Tested: * - AIEngine ↔ KnowledgeBase (hypothesis generation with domain knowledge) * - ExperimentEngine ↔ SafetySystem (automated experimentation with safety protocols) * - AnalysisEngine ↔ ValidationEngine (statistical analysis with reproducibility checks) * - MLEngine ↔ InterpretabilityTools (model training with explainable results) * - CausalInference ↔ DomainKnowledge (causal reasoning with scientific constraints) * * This design promotes rigorous scientific methodology while leveraging AI * for research acceleration and enhanced reproducibility. */