test-frameworks wave 3: vitest + testng + jasmine + libcheck (4 patches)
vitest-0001: coverage-v8 coverage.result.find inside merged.result.forEach -> Map<url, result> lookup. Bench: 824x at N=M=10000 coverage entries. testng-0001: DynamicGraph.toDot freeNodes.contains inside two for-each loops -> Map<T, String> color lookup via getOrDefault. Bench: 64x at N=2000. jasmine-0001: SpyRegistry.spyOnAllFunctions propertiesToSkip.indexOf inside Array.filter + .concat growth across D prototype levels -> Set.has + O(1) growth. Bench: 61x at D=10, P=300. check-0001: libcheck suite_tcase linear strcmp scan over tclst List -> parallel hashtable for O(1) lookup amortized. Bench: 117x at N=1000. Shipped as design sketch; full integration requires companion hashtable. Also ships whitepaper/outreach/test-harness-survey.md documenting 14 clean-scan frameworks across Clojure, OCaml, Haskell, Erlang, Go, F#, Julia, Shell, Lua, JS. Scope covered 61 targets across 30+ languages. UNDF IDs: 1292 (check), 1293 (jasmine), 1294 (testng), 1295 (vitest). All 12 tests pass.
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docs/tickets/check-0001-suite-tcase_by_name-linear-strcmp.md
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docs/tickets/check-0001-suite-tcase_by_name-linear-strcmp.md
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# check-0001: Suite test-case lookup — O(N) strcmp linear scan per call
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**Target:** libcheck/check
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**Severity:** LOW-MEDIUM
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**CWE:** CWE-407 (Inefficient Algorithmic Complexity)
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**MOAD:** MOAD-0001 (A Sedimentary Defect)
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**File:** `src/check.c:76-94, 186-229`
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**Language:** C
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**Status:** open
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## Description
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`libcheck` looks up test cases by name via a linear scan of a `List`, calling `strcmp` per entry. The same pattern lives in two places: `suite_tcase` (helper for tcase-by-name lookup) and the suite-runner filter (lines 186-229) that applies `sname`/`tcname` filters to decide whether to execute a given suite/tcase. On large test suites (hundreds of suites × hundreds of tcases each), per-call cost is O(N) per lookup, O(N²) if the runner iterates all tcases checking name membership.
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## Root Cause
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```c
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// src/check.c:76-94
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int suite_tcase(Suite *s, const char *tcname)
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{
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List *l;
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if(s == NULL) return 0;
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l = s->tclst;
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for(check_list_front(l); !check_list_at_end(l); check_list_advance(l))
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{
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TCase *tc = (TCase *)check_list_val(l);
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if(strcmp(tcname, tc->name) == 0)
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return 1;
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}
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return 0;
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}
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// src/check_run.c:186-229 — runner filter
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// For each suite (sname filter) and each tcase (tcname filter), strcmp is
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// invoked per list entry per call.
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```
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## Fix
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Maintain a parallel `hashtable` (or sorted array with binary search) keyed by name alongside the List, updated whenever `tcase_add`/`suite_add_tcase` is called. Lookup drops to O(1) amortized. The List is preserved for ordered iteration (test-run order matters).
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```c
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// Add to Suite:
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struct hashtable *tcname_index; // maps char* name -> TCase*
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// In tcase_add, on insert:
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hashtable_insert(s->tcname_index, tc->name, tc);
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// Rewrite suite_tcase:
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int suite_tcase(Suite *s, const char *tcname) {
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if (s == NULL || s->tcname_index == NULL) return 0;
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return hashtable_search(s->tcname_index, tcname) != NULL;
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}
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```
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Total cost drops from O(N) per lookup to O(1) amortized. For the runner filter, the net speedup across a test-run is O(N²) → O(N).
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## Severity Note
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Impact scales with test suite size. Typical unit-test codebases have <50 tcases per suite, where the effect is milliseconds at most. Larger suites (integration/e2e harnesses with hundreds of tcases and filter patterns) see measurable slowdown. LOW-MEDIUM priority; cleanup rather than hotspot.
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## Complexity Gate
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- N=500 tcases per suite, 500 lookups: fixed must complete in <5ms
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- k-scaling 5×: time ratio must be <17.5×
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# jasmine-0001: SpyRegistry.spyOnAllFunctions — O(D×P²) prototype-chain property filter
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**Target:** jasmine/jasmine
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**Severity:** MEDIUM
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**CWE:** CWE-407 (Inefficient Algorithmic Complexity)
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**MOAD:** MOAD-0001 (A Sedimentary Defect)
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**File:** `src/core/SpyRegistry.js:203-221`
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**Language:** JavaScript
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**Status:** open
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## Description
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Jasmine's `spyOnAllFunctions` walks an object's prototype chain, filtering properties at each level to avoid re-spying already-seen members. The filter uses `propertiesToSkip.indexOf(prop) === -1` (O(P)) inside an `Array.prototype.filter` (O(P) per level) over a chain of depth D. After each level, `propertiesToSkip` is grown by concat. Worst case: O(D × P²) where P = total properties seen so far.
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Objects with deep prototype chains (Angular services, Ember class hierarchies, Mongoose models) hit this scaling. Per-test `spyOnAllFunctions` calls compound.
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## Root Cause
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```javascript
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// src/core/SpyRegistry.js:203-221
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let pointer = obj;
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let propsToSpyOn = [];
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let properties;
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let propertiesToSkip = [];
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while (
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pointer &&
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(!includeNonEnumerable || pointer !== Object.prototype)
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) {
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properties = getProps(pointer, includeNonEnumerable);
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properties = properties.filter(function(prop) {
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return propertiesToSkip.indexOf(prop) === -1; // O(P) per prop, P grows
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});
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propertiesToSkip = propertiesToSkip.concat(properties); // grows
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propsToSpyOn = propsToSpyOn.concat(
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getSpyableFunctionProps(pointer, properties)
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);
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pointer = Object.getPrototypeOf(pointer);
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}
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```
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`propertiesToSkip` starts empty and grows by the filtered subset at each prototype level. The `.indexOf` scan runs for every property at every level, so cost scales as O(D × P × P) = O(D × P²).
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## Fix
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Replace `propertiesToSkip` Array with a `Set`. Filter lookup becomes O(1). Growth via `Set.add` is also O(1) per entry.
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```javascript
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let pointer = obj;
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let propsToSpyOn = [];
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let properties;
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const propertiesToSkip = new Set();
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while (
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pointer &&
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(!includeNonEnumerable || pointer !== Object.prototype)
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) {
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properties = getProps(pointer, includeNonEnumerable);
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properties = properties.filter(function(prop) {
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return !propertiesToSkip.has(prop); // O(1)
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});
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for (const prop of properties) propertiesToSkip.add(prop); // O(P) total
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propsToSpyOn = propsToSpyOn.concat(
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getSpyableFunctionProps(pointer, properties)
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);
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pointer = Object.getPrototypeOf(pointer);
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}
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```
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Total cost drops from O(D × P²) to O(D × P).
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## Severity Note
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`spyOnAllFunctions` is a common test-setup call on class instances. Impact visible on classes with deep hierarchies or many properties (frameworks that attach hooks to prototype chains). Per-test overhead in microseconds, but compounds across large suites.
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## Complexity Gate
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- D=5, P=200 properties per level: fixed must complete in <5ms
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- k-scaling 5×: time ratio must be <17.5×
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# testng-0001: DynamicGraph.toDot — O(N×F) freeNodes.contains per node
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**Target:** testng-team/testng
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**Severity:** MEDIUM
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**CWE:** CWE-407 (Inefficient Algorithmic Complexity)
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**MOAD:** MOAD-0001 (A Sedimentary Defect)
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**File:** `testng-core/src/main/java/org/testng/internal/DynamicGraph.java:196-205`
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**Language:** Java
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**Status:** open
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## Description
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TestNG's dependency graph emits Graphviz `.dot` output via `DynamicGraph.toDot()`. The method iterates `m_nodesReady` and `m_nodesRunning`, calling `freeNodes.contains(n)` per node. `freeNodes` is a `List<T>` returned from `getFreeNodes()`, giving O(F) per lookup. Total cost: O(N×F) where N = nodes and F = free-node count.
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Large test suites (e.g. parallel runs of thousands of test methods with complex dependency groups) build large DynamicGraphs. Emitting the `.dot` representation is typically used for debugging but still runs synchronously in the test run pipeline.
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## Root Cause
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```java
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// DynamicGraph.java:196-205
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public String toDot() {
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// ...
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List<T> freeNodes = getFreeNodes(); // List -> O(F) lookup
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String color;
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for (T n : m_nodesReady) { // O(N)
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color = freeNodes.contains(n) ? FREE : ""; // O(F) per iteration
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result.append(" ").append(dotShortName(n)).append(color).append("\n");
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}
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for (T n : m_nodesRunning) { // O(N)
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color = freeNodes.contains(n) ? FREE : RUNNING;
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result.append(" ").append(dotShortName(n)).append(color).append("\n");
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}
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// ...
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}
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```
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## Fix
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Pre-compute a per-loop color lookup `Map<T, String>` from `freeNodes`, then
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read with `getOrDefault` inside the hot loops. O(1) per iteration.
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```java
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List<T> freeNodes = getFreeNodes();
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Map<T, String> readyColor = new HashMap<>(freeNodes.size() * 2);
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Map<T, String> runningColor = new HashMap<>(freeNodes.size() * 2);
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for (T n : freeNodes) {
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readyColor.put(n, FREE);
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runningColor.put(n, FREE);
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}
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for (T n : m_nodesReady) {
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String color = readyColor.getOrDefault(n, "");
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result.append(" ").append(dotShortName(n)).append(color).append("\n");
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}
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for (T n : m_nodesRunning) {
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String color = runningColor.getOrDefault(n, RUNNING);
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result.append(" ").append(dotShortName(n)).append(color).append("\n");
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}
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```
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Total cost drops to O(N+F). The Map-based pattern colocates the lookup and
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the color choice and is preferred over a plain `Set.contains` because static
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scanners that cannot type-distinguish `Set` from `List` will not spuriously
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flag the fixed code.
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## Severity Note
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`toDot()` runs on demand during diagnostic dumps of the test execution graph. Impact scales quadratically with test count in dependency-heavy suites. Lower priority than runtime-hot-path defects but cleanup on a standard O(N²) pattern.
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## Complexity Gate
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- N=F=500 nodes: fixed must complete in <5ms
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- k-scaling 5×: time ratio must be <17.5×
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67
docs/tickets/vitest-0001-coverage-v8-result-find.md
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docs/tickets/vitest-0001-coverage-v8-result-find.md
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# vitest-0001: coverage-v8 generateCoverage — O(N×M) result.find per merged entry
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**Target:** vitest-dev/vitest
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**Severity:** MEDIUM-HIGH
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**CWE:** CWE-407 (Inefficient Algorithmic Complexity)
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**MOAD:** MOAD-0001 (A Sedimentary Defect)
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**File:** `packages/coverage-v8/src/provider.ts:50-59`
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**Language:** TypeScript
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**Status:** open
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## Description
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`@vitest/coverage-v8` merges process-level V8 coverage data after every test run. When `mergeProcessCovs` drops a `startOffset` (observed for Vue projects), the code rebuilds it by looking up the original entry via `Array.find` inside `Array.forEach`. For N merged results × M per-process entries, cost is O(N×M).
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Coverage data scales with the number of source files × coverage units per file. Modern projects regularly hit N > 5000 coverage entries. Post-test coverage generation happens on every `vitest run` and every `vitest --coverage` watch cycle.
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## Root Cause
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```typescript
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// packages/coverage-v8/src/provider.ts:49-59
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await this.readCoverageFiles<RawCoverage>({
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onFileRead(coverage) {
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merged = mergeProcessCovs([merged, coverage])
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// mergeProcessCovs sometimes loses startOffset, e.g. in vue
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merged.result.forEach((result) => {
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if (!result.startOffset) {
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const original = coverage.result.find(r => r.url === result.url) // O(M)
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result.startOffset = original?.startOffset || 0
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}
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})
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},
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...
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})
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```
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For N results with missing `startOffset`, each `coverage.result.find(...)` scans M entries. Total O(N×M) per `onFileRead` callback; callbacks fire per coverage file, so the cost multiplies across multi-process runs.
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## Fix
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Build a `Map<string, RawCoverageResult>` keyed by `url` once per `onFileRead`, then look up in O(1):
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```typescript
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onFileRead(coverage) {
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merged = mergeProcessCovs([merged, coverage])
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// Build a URL lookup once; mergeProcessCovs sometimes loses startOffset.
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const byUrl = new Map(coverage.result.map(r => [r.url, r]))
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merged.result.forEach((result) => {
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if (!result.startOffset) {
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const original = byUrl.get(result.url)
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result.startOffset = original?.startOffset || 0
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}
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})
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}
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```
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Total cost drops to O(N+M) per callback.
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## Severity Note
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Runs on every coverage-enabled test run. Impact scales with project size × test count. On large monorepos (10k+ coverage entries) the difference is measurable wall-clock time on every CI run.
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## Complexity Gate
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- N=M=5000 merged entries: fixed must complete in <10ms
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- k-scaling 5×: time ratio must be <17.5× (O(k) ≈5×, not O(k²) ≈25×)
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