108 lines
4.2 KiB
JavaScript
108 lines
4.2 KiB
JavaScript
// distribution-stats.test.mjs — Tests for scanners/lib/distribution-stats.mjs
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// Zero external dependencies: node:test + node:assert only.
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import { describe, it } from 'node:test';
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import assert from 'node:assert/strict';
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import { jensenShannonDivergence, buildDistribution } from '../../scanners/lib/distribution-stats.mjs';
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// ---------------------------------------------------------------------------
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// buildDistribution
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// ---------------------------------------------------------------------------
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describe('distribution-stats — buildDistribution', () => {
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it('empty array → empty map', () => {
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const d = buildDistribution([]);
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assert.equal(d.size, 0);
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});
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it('single category normalizes to 1.0', () => {
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const d = buildDistribution(['Read', 'Read', 'Read']);
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assert.equal(d.size, 1);
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assert.equal(d.get('Read'), 1.0);
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});
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it('two equal categories normalize to 0.5 each', () => {
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const d = buildDistribution(['Read', 'Bash', 'Read', 'Bash']);
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assert.equal(d.size, 2);
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assert.equal(d.get('Read'), 0.5);
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assert.equal(d.get('Bash'), 0.5);
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});
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it('unequal distribution normalizes correctly', () => {
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const d = buildDistribution(['Read', 'Read', 'Read', 'Bash']);
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assert.equal(d.get('Read'), 0.75);
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assert.equal(d.get('Bash'), 0.25);
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});
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it('sum of probabilities equals 1.0', () => {
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const d = buildDistribution(['Read', 'Bash', 'Write', 'Grep', 'Bash']);
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let sum = 0;
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for (const v of d.values()) sum += v;
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assert.ok(Math.abs(sum - 1.0) < 1e-10, `Sum ${sum} should be ~1.0`);
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});
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});
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// ---------------------------------------------------------------------------
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// jensenShannonDivergence
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// ---------------------------------------------------------------------------
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describe('distribution-stats — jensenShannonDivergence', () => {
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it('identical distributions → JSD = 0', () => {
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const P = buildDistribution(['Read', 'Bash', 'Read', 'Bash']);
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const Q = buildDistribution(['Read', 'Bash', 'Read', 'Bash']);
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const jsd = jensenShannonDivergence(P, Q);
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assert.ok(Math.abs(jsd) < 1e-10, `JSD ${jsd} should be ~0`);
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});
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it('fully disjoint distributions → JSD = 1', () => {
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const P = buildDistribution(['Read', 'Read', 'Read']);
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const Q = buildDistribution(['Bash', 'Bash', 'Bash']);
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const jsd = jensenShannonDivergence(P, Q);
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assert.ok(Math.abs(jsd - 1.0) < 1e-10, `JSD ${jsd} should be ~1.0`);
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});
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it('partially overlapping distributions → 0 < JSD < 1', () => {
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const P = buildDistribution(['Read', 'Read', 'Bash']);
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const Q = buildDistribution(['Read', 'Bash', 'Bash']);
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const jsd = jensenShannonDivergence(P, Q);
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assert.ok(jsd > 0, `JSD ${jsd} should be > 0`);
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assert.ok(jsd < 1, `JSD ${jsd} should be < 1`);
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});
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it('JSD is symmetric: JSD(P,Q) = JSD(Q,P)', () => {
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const P = buildDistribution(['Read', 'Read', 'Read', 'Bash']);
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const Q = buildDistribution(['Read', 'Bash', 'Bash', 'Bash']);
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const jsd1 = jensenShannonDivergence(P, Q);
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const jsd2 = jensenShannonDivergence(Q, P);
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assert.ok(Math.abs(jsd1 - jsd2) < 1e-10, `JSD(P,Q)=${jsd1} should equal JSD(Q,P)=${jsd2}`);
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});
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it('two empty distributions → JSD = 0', () => {
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const P = new Map();
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const Q = new Map();
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const jsd = jensenShannonDivergence(P, Q);
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assert.equal(jsd, 0);
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});
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it('one empty + one non-empty → JSD = 0.5', () => {
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const P = buildDistribution(['Read']);
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const Q = new Map();
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const jsd = jensenShannonDivergence(P, Q);
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assert.ok(Math.abs(jsd - 0.5) < 1e-10, `JSD ${jsd} should be 0.5`);
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});
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it('three categories with different distributions', () => {
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const P = buildDistribution(['Read', 'Read', 'Read', 'Write', 'Write', 'Bash']);
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const Q = buildDistribution(['Read', 'Write', 'Write', 'Write', 'Bash', 'Bash']);
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const jsd = jensenShannonDivergence(P, Q);
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assert.ok(jsd > 0, `JSD ${jsd} should be > 0`);
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assert.ok(jsd < 1, `JSD ${jsd} should be < 1`);
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});
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it('diverse vs concentrated → high JSD', () => {
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const P = buildDistribution(['Read', 'Write', 'Bash', 'Grep', 'Glob']);
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const Q = buildDistribution(['Read', 'Read', 'Read', 'Read', 'Read']);
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const jsd = jensenShannonDivergence(P, Q);
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assert.ok(jsd > 0.3, `JSD ${jsd} should be > 0.3 for diverse vs concentrated`);
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});
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});
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