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test: add unit tests for v1.2.9 features
- ConfigDefaultsTest: validates XNNPACK thread defaults (1-8 range), short gesture distance settings (min/max range validation), and neural prediction parameter bounds - ContractionFrequencyTest: verifies French contraction frequency scoring logic, tests that high-frequency contractions like "qu'est" rank higher than lower-frequency base words like "quest" when using dictionary frequency lookups Coverage for v1.2.9 features: - Tensor reuse: already covered by MemoryPoolTest.kt - XNNPACK threads: ConfigDefaultsTest validates default and range - French contraction frequency: ContractionFrequencyTest validates scoring - Short gesture max distance: ConfigDefaultsTest validates default/range — claude-opus-4-5-20251101
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‎src/test/kotlin/tribixbite/cleverkeys/ConfigDefaultsTest.kt‎

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package tribixbite.cleverkeys
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import com.google.common.truth.Truth.assertThat
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import org.junit.Test
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/**
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* Pure JVM tests for Config.Defaults values.
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*
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* Tests validate that default values are within expected ranges
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* and match documented behavior for v1.2.9 features.
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*/
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class ConfigDefaultsTest {
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// =========================================================================
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// ONNX XNNPACK Thread Settings (v1.2.9)
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// =========================================================================
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@Test
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fun `XNNPACK threads default is 2`() {
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assertThat(Defaults.ONNX_XNNPACK_THREADS).isEqualTo(2)
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}
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@Test
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fun `XNNPACK threads default is within valid range 1-8`() {
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assertThat(Defaults.ONNX_XNNPACK_THREADS).isAtLeast(1)
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assertThat(Defaults.ONNX_XNNPACK_THREADS).isAtMost(8)
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}
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// =========================================================================
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// Short Gesture Settings (v1.2.9)
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// =========================================================================
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@Test
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fun `short gestures enabled by default`() {
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assertThat(Defaults.SHORT_GESTURES_ENABLED).isTrue()
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}
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@Test
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fun `short gesture min distance default is 28`() {
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assertThat(Defaults.SHORT_GESTURE_MIN_DISTANCE).isEqualTo(28)
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}
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@Test
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fun `short gesture max distance default is 141`() {
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assertThat(Defaults.SHORT_GESTURE_MAX_DISTANCE).isEqualTo(141)
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}
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@Test
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fun `short gesture min is less than max`() {
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assertThat(Defaults.SHORT_GESTURE_MIN_DISTANCE)
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.isLessThan(Defaults.SHORT_GESTURE_MAX_DISTANCE)
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}
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@Test
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fun `short gesture min distance is within valid range 10-95`() {
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assertThat(Defaults.SHORT_GESTURE_MIN_DISTANCE).isAtLeast(10)
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assertThat(Defaults.SHORT_GESTURE_MIN_DISTANCE).isAtMost(95)
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}
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@Test
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fun `short gesture max distance is within valid range 50-200`() {
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assertThat(Defaults.SHORT_GESTURE_MAX_DISTANCE).isAtLeast(50)
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assertThat(Defaults.SHORT_GESTURE_MAX_DISTANCE).isAtMost(200)
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}
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// =========================================================================
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// Neural Prediction Defaults
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// =========================================================================
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@Test
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fun `neural beam width default is 6`() {
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assertThat(Defaults.NEURAL_BEAM_WIDTH).isEqualTo(6)
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}
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@Test
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fun `neural max length default is 20`() {
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assertThat(Defaults.NEURAL_MAX_LENGTH).isEqualTo(20)
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}
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@Test
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fun `neural batch beams disabled by default`() {
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assertThat(Defaults.NEURAL_BATCH_BEAMS).isFalse()
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}
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@Test
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fun `neural frequency weight default is 0_57`() {
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assertThat(Defaults.NEURAL_FREQUENCY_WEIGHT).isWithin(0.01f).of(0.57f)
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}
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// =========================================================================
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// Swipe Typing Defaults
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// =========================================================================
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@Test
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fun `swipe typing enabled by default`() {
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assertThat(Defaults.SWIPE_TYPING_ENABLED).isTrue()
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}
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@Test
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fun `swipe trail enabled by default`() {
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assertThat(Defaults.SWIPE_TRAIL_ENABLED).isTrue()
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}
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@Test
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fun `word prediction enabled by default`() {
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assertThat(Defaults.WORD_PREDICTION_ENABLED).isTrue()
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}
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// =========================================================================
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// Backup/Restore compatibility (v1.2.9)
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// =========================================================================
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@Test
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fun `all neural defaults have valid values for backup restore`() {
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// These are used in BackupRestoreManager and must be reasonable
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assertThat(Defaults.NEURAL_BEAM_WIDTH).isAtLeast(1)
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assertThat(Defaults.NEURAL_BEAM_WIDTH).isAtMost(20)
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assertThat(Defaults.NEURAL_MAX_LENGTH).isAtLeast(5)
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assertThat(Defaults.NEURAL_MAX_LENGTH).isAtMost(50)
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assertThat(Defaults.NEURAL_CONFIDENCE_THRESHOLD).isAtLeast(0f)
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assertThat(Defaults.NEURAL_CONFIDENCE_THRESHOLD).isAtMost(1f)
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}
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}

‎src/test/kotlin/tribixbite/cleverkeys/ContractionFrequencyTest.kt‎

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package tribixbite.cleverkeys
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import com.google.common.truth.Truth.assertThat
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import org.junit.Test
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/**
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* Tests for French contraction frequency scoring (v1.2.9 fix).
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*
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* The v1.2.9 fix ensures that French contractions like "qu'est" can rank
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* higher than their base words like "quest" when the contraction has
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* higher frequency in the dictionary.
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*
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* This is achieved by looking up actual contraction frequencies and using
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* VocabularyUtils.calculateCombinedScore for fair comparison.
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*/
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class ContractionFrequencyTest {
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// =========================================================================
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// calculateCombinedScore for contraction ranking
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// =========================================================================
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@Test
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fun `high frequency contraction scores higher than low frequency base word`() {
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// Simulate "qu'est" (high frequency French contraction) vs "quest" (lower frequency)
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val contractionFrequency = 0.9f // Very common French word
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val baseWordFrequency = 0.3f // Less common English word
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val commonBoost = 1.15f
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val confidenceWeight = 0.6f
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val frequencyWeight = 0.4f
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// Both have same NN confidence (user swiped the same pattern)
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val nnConfidence = 0.85f
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val contractionScore = VocabularyUtils.calculateCombinedScore(
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nnConfidence, contractionFrequency, commonBoost, confidenceWeight, frequencyWeight
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)
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val baseWordScore = VocabularyUtils.calculateCombinedScore(
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nnConfidence, baseWordFrequency, 1.0f, confidenceWeight, frequencyWeight
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)
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// Contraction should score higher due to higher frequency + common boost
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assertThat(contractionScore).isGreaterThan(baseWordScore)
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}
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@Test
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fun `low frequency contraction scores lower than high frequency base word`() {
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// When base word is more common, it should rank higher
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val contractionFrequency = 0.2f // Rare contraction
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val baseWordFrequency = 0.95f // Very common word
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val top5000Boost = 1.08f
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val commonBoost = 1.15f
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val confidenceWeight = 0.6f
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val frequencyWeight = 0.4f
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val nnConfidence = 0.85f
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val contractionScore = VocabularyUtils.calculateCombinedScore(
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nnConfidence, contractionFrequency, top5000Boost, confidenceWeight, frequencyWeight
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)
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val baseWordScore = VocabularyUtils.calculateCombinedScore(
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nnConfidence, baseWordFrequency, commonBoost, confidenceWeight, frequencyWeight
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)
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// Base word should score higher
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assertThat(baseWordScore).isGreaterThan(contractionScore)
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}
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@Test
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fun `frequency ranking is fair with equal boosts`() {
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// With same boosts, higher frequency should always win
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val boost = 1.0f
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val confidenceWeight = 0.6f
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val frequencyWeight = 0.4f
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val nnConfidence = 0.80f
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val highFreqScore = VocabularyUtils.calculateCombinedScore(
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nnConfidence, 0.9f, boost, confidenceWeight, frequencyWeight
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)
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val lowFreqScore = VocabularyUtils.calculateCombinedScore(
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nnConfidence, 0.3f, boost, confidenceWeight, frequencyWeight
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)
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assertThat(highFreqScore).isGreaterThan(lowFreqScore)
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}
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// =========================================================================
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// Frequency conversion from rank
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// =========================================================================
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@Test
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fun `frequency rank 0 converts to frequency 1_0`() {
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// Rank 0 = most common word, should have frequency 1.0
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val rank = 0
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val frequency = 1.0f - (rank / 255.0f)
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assertThat(frequency).isWithin(0.001f).of(1.0f)
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}
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@Test
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fun `frequency rank 255 converts to near zero frequency`() {
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// Rank 255 = rarest tracked word
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val rank = 255
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val frequency = 1.0f - (rank / 255.0f)
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assertThat(frequency).isWithin(0.001f).of(0.0f)
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}
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@Test
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fun `frequency rank 127 converts to mid frequency`() {
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// Rank 127 = middle of the range
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val rank = 127
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val frequency = 1.0f - (rank / 255.0f)
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assertThat(frequency).isWithin(0.01f).of(0.5f)
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}
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// =========================================================================
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// Realistic French contraction scenarios
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// =========================================================================
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@Test
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fun `quest_vs_quest_scenario`() {
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// In French, "qu'est" (what is) is more common than English "quest"
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// Simulated dictionary ranks:
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// - "qu'est" rank ~20 (very common in French)
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// - "quest" rank ~150 (moderately common in English)
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val questRank = 150
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val questFreq = 1.0f - (questRank / 255.0f) // ~0.41
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val questApostropheRank = 20
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val questApostropheFreq = 1.0f - (questApostropheRank / 255.0f) // ~0.92
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val commonBoost = 1.15f
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val top5000Boost = 1.08f
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val confidenceWeight = 0.6f
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val frequencyWeight = 0.4f
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val nnConfidence = 0.82f
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val questScore = VocabularyUtils.calculateCombinedScore(
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nnConfidence, questFreq, top5000Boost, confidenceWeight, frequencyWeight
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)
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val questApostropheScore = VocabularyUtils.calculateCombinedScore(
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nnConfidence, questApostropheFreq, commonBoost, confidenceWeight, frequencyWeight
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)
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// "qu'est" should rank higher due to its much higher frequency
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assertThat(questApostropheScore).isGreaterThan(questScore)
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}
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@Test
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fun `jai_vs_jail_scenario`() {
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// French "j'ai" (I have) vs English "jail"
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// "j'ai" is extremely common in French
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// "jail" is moderately common in English
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val jailRank = 180
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val jailFreq = 1.0f - (jailRank / 255.0f) // ~0.29
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val jaiRank = 5
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val jaiFreq = 1.0f - (jaiRank / 255.0f) // ~0.98
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val commonBoost = 1.15f
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val top5000Boost = 1.08f
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val confidenceWeight = 0.6f
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val frequencyWeight = 0.4f
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val nnConfidence = 0.78f
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val jailScore = VocabularyUtils.calculateCombinedScore(
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nnConfidence, jailFreq, top5000Boost, confidenceWeight, frequencyWeight
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)
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val jaiScore = VocabularyUtils.calculateCombinedScore(
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nnConfidence, jaiFreq, commonBoost, confidenceWeight, frequencyWeight
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)
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// "j'ai" should rank higher
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assertThat(jaiScore).isGreaterThan(jailScore)
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}
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// =========================================================================
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// Edge cases
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// =========================================================================
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@Test
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fun `zero frequency contraction still gets valid score`() {
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val score = VocabularyUtils.calculateCombinedScore(
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confidence = 0.8f,
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frequency = 0.0f,
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boost = 1.0f,
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confidenceWeight = 0.6f,
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frequencyWeight = 0.4f
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)
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// Should still produce a positive score from confidence alone
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assertThat(score).isGreaterThan(0f)
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assertThat(score).isWithin(0.001f).of(0.48f) // 0.6 * 0.8 + 0.4 * 0
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}
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@Test
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fun `default contraction frequency 0_6 is reasonable fallback`() {
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// When contraction isn't in dictionary, default to 0.6
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val defaultFreq = 0.6f
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val score = VocabularyUtils.calculateCombinedScore(
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confidence = 0.8f,
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frequency = defaultFreq,
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boost = 1.08f,
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confidenceWeight = 0.6f,
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frequencyWeight = 0.4f
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)
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// Score should be reasonable (not too high, not too low)
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assertThat(score).isGreaterThan(0.5f)
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assertThat(score).isLessThan(1.0f)
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}
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}

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