Pre-compute spatial graph statistics during Data initialization#65
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Pre-compute spatial graph statistics during Data initialization#65
Data initialization#65Conversation
Instead of calculating average degree and starting positions for each spatial match, pre-compute these statistics once when loading adjacency graphs. This avoids repeated map/inject operations on graph data during password matching, improving performance by approximately 9.3%. Performance improvement: 0.097ms -> 0.088ms per password (9.3% faster)
Added test coverage for previously untested Math module methods: lg (logarithm base 2): - Powers of 2 (exact values) - Non-power-of-2 values (with tolerance) - Decimal values (negative logs) nCk (combinations): - Edge cases (k > n, k = 0) - Small combinations (n=5) - Larger values (poker hands: 52 choose 5) - Symmetry property verification - Basic edge cases Test count increased from 18 to 27 examples.
Created test suite for previously untested Data class covering: Initialization: - Dictionary loading (5 expected dictionaries) - Adjacency graph loading (4 expected graphs) - Trie building for all dictionaries - Graph statistics pre-computation Graph statistics: - Verification of average_degree values - Verification of starting_positions values - Correctness checks for qwerty and keypad Ranked dictionaries: - Word ranking verification - Common password frequency checks Custom word lists: - Dictionary addition via add_word_list - Trie generation for custom dictionaries - Word searchability via tries - Empty list handling Test count increased from 271 to 291 examples (20 new tests).
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Context
The spatial entropy calculation uses graph statistics (average degree and starting positions) to compute password strength for keyboard patterns. Previously, these statistics were calculated on-demand for every spatial match by iterating through the adjacency graph data, performing map/compact/inject operations each time.
Since adjacency graphs are immutable after loading, these statistics can be computed once during initialisation and reused, eliminating redundant calculations.
Changes
Data class (
lib/zxcvbn/data.rb):compute_graph_statsprivate method that pre-computes average degree and starting positions for all adjacency graphs during initialisation@graph_statshash with structure:{ graph_name => { average_degree:, starting_positions: } }graph_statsreaderMath module (
lib/zxcvbn/math.rb):average_degree_for_graphto retrieve pre-computed value fromdata.graph_statsstarting_positions_for_graphto retrieve pre-computed value fromdata.graph_statsPerformance
Benchmark results (1000 iterations across 10 passwords with spatial patterns):
The improvement comes from eliminating repeated iterations over adjacency graph data during entropy calculations. All 262 tests pass, confirming correctness is maintained.