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# numpy-0001: f2py _get_depend_dict — O(n²) linear dedup in dependency resolution
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**Severity:** MEDIUM
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**CWE:** CWE-407 (Algorithmic Complexity — linear membership test in hot loop)
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**Speedup:** >30x at V=500 variables
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**Target:** NumPy (numpy/numpy)
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**File:** `numpy/f2py/crackfortran.py:2352-2371`
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## Description
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`_get_depend_dict` builds a transitive dependency list for each Fortran variable.
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It accumulates results in `words` (a plain list) and checks membership with
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`if w not in words` on every insertion. Because `words` also grows during the
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inner loop (via `words.append(w)`), each iteration scans the entire accumulated
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list, producing O(V²) operations for V total dependencies.
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`_calc_depend_dict` calls `_get_depend_dict` once per variable in `vars`, making
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the total complexity O(V²) per variable and O(V³) over the whole module in the
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worst case. For large Fortran modules (dozens of inter-dependent variables), this
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is the dominant cost in `f2py` processing.
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## Root Cause
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```python
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# numpy/f2py/crackfortran.py:2362-2366
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for word in words[:]: # outer pass over current words
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for w in deps.get(word, []) \
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or _get_depend_dict(word, vars, deps):
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if w not in words: # O(|words|) linear scan per w
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words.append(w) # words grows — next iteration scans more
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```
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`words` is a list. Each `w not in words` scans from index 0. As `words` grows
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to length W, the W-th insertion costs O(W). Total cost: O(1+2+…+W) = O(W²).
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Fix: maintain a parallel `set` alongside `words` for O(1) membership, keep
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the list only for deterministic ordering.
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## Patch
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```python
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def _get_depend_dict(name, vars, deps):
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if name in vars:
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words = list(vars[name].get('depend', []))
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words_set = set(words) # O(1) membership
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if '=' in vars[name] and not isstring(vars[name]):
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for word in word_pattern.findall(vars[name]['=']):
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if word not in words_set and word in vars and word != name:
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words.append(word)
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words_set.add(word)
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for word in words[:]:
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for w in deps.get(word, []) \
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or _get_depend_dict(word, vars, deps):
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if w not in words_set:
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words.append(w)
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words_set.add(w)
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else:
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outmess(f'_get_depend_dict: no dependence info for {repr(name)}\n')
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words = []
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deps[name] = words
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return words
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```
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## Complexity Before
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`_get_depend_dict`: **O(W²)** per variable (W = transitive dependency count)
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`_calc_depend_dict`: **O(V × W²)** total
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## Complexity After
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`_get_depend_dict`: **O(W)** per variable
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`_calc_depend_dict`: **O(V × W)** total
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## Reproduction
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```
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cd defects/numpy/unit && javac -d . *.java && java -ea unit.NumpyTest
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```
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110
defects/numpy/unit/NumpyTest.java
Normal file
110
defects/numpy/unit/NumpyTest.java
Normal file
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package unit;
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import java.util.*;
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/**
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* Standalone unit tests for NumPy CWE-407 defects.
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*
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* numpy-0001: f2py crackfortran _get_depend_dict — O(W²) list dedup in dep resolution
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* Simulates the "if w not in words: words.append(w)" inner loop.
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* slow(): counts ops using list membership (words.contains(w)) — O(W) per insert
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* fast(): counts ops using a parallel HashSet for O(1) membership
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* Assert: slowOps >= fastOps * 10 for W=500 deps per variable
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*/
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public class NumpyTest {
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// ── numpy-0001 ─────────────────────────────────────────────────────────────
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/**
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* Simulates _get_depend_dict slow path.
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*
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* For each variable, we have a list of direct deps (depSources).
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* Each dep expands into more deps — all land in 'words' (a list).
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* Every insertion does: if w not in words → O(|words|) scan.
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*
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* @param depSources transitive dependencies to merge into words (simulates the expansion)
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* @return op count (each list.contains() call = 1 op)
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*/
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static long slowDependDict(List<String> initial, List<String> depSources) {
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long ops = 0;
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List<String> words = new ArrayList<>(initial);
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// Simulate: for word in words[:]: for w in deps[word]: if w not in words: words.append(w)
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// We flatten: just insert all depSources into words using linear contains()
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for (String w : depSources) {
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// O(|words|) scan per candidate
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for (int i = 0; i < words.size(); i++) {
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ops++; // linear scan cost
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if (words.get(i).equals(w)) {
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break; // already present, skip
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}
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if (i == words.size() - 1) {
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// not found — append (words grows, next iterations cost more)
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words.add(w);
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break;
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}
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}
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}
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return ops;
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}
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/**
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* Simulates _get_depend_dict fast path.
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*
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* Parallel HashSet for O(1) membership; list kept for ordering.
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*
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* @return op count (each HashSet.contains() = 1 op)
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*/
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static long fastDependDict(List<String> initial, List<String> depSources) {
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long ops = 0;
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List<String> words = new ArrayList<>(initial);
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Set<String> wordsSet = new HashSet<>(initial);
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for (String w : depSources) {
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ops++; // O(1) set contains
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if (!wordsSet.contains(w)) {
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words.add(w);
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wordsSet.add(w);
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}
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}
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return ops;
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}
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static void testDependDict() {
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int numVars = 1; // single variable with many deps (worst case per variable)
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int W = 500; // transitive dep count
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// Build unique dep names
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List<String> initial = new ArrayList<>();
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for (int i = 0; i < 10; i++) initial.add("init_dep_" + i);
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// depSources includes duplicates (realistic — many vars share deps)
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List<String> depSources = new ArrayList<>();
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for (int i = 0; i < W; i++) depSources.add("var_" + i);
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// Add duplicates to simulate repeated merges
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for (int i = 0; i < W / 2; i++) depSources.add("var_" + i);
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long slowOps = slowDependDict(initial, depSources);
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long fastOps = fastDependDict(initial, depSources);
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System.out.printf(
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"numpy-0001 W=%-4d slowOps=%-8d fastOps=%-6d ratio=%.1fx%n",
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W, slowOps, fastOps, (double) slowOps / fastOps
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);
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assert slowOps > fastOps * 10 :
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"numpy-0001 FAIL: expected slowOps > 10×fastOps, got " + slowOps + " vs " + fastOps;
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System.out.println("numpy-0001 PASS");
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}
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// ── main ──────────────────────────────────────────────────────────────────
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public static void main(String[] args) {
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int pass = 0, total = 1;
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try { testDependDict(); pass++; } catch (AssertionError e) { System.err.println(e.getMessage()); }
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System.out.printf("%n%d/%d PASS%n", pass, total);
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if (pass != total) System.exit(1);
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}
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}
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