@clduab11/gemini-flow
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Revolutionary AI agent swarm coordination platform with Google Services integration, multimedia processing, and production-ready monitoring. Features 8 Google AI services, quantum computing capabilities, and enterprise-grade security.
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text/typescript
/**
* 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<any>;
let mockAIEngine: jest.Mocked<any>;
let mockExperimentEngine: jest.Mocked<any>;
let mockAnalysisEngine: jest.Mocked<any>;
let mockValidationEngine: jest.Mocked<any>;
let mockKnowledgeBase: jest.Mocked<any>;
let mockPerformanceMonitor: jest.Mocked<any>;
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