From 26cc6cce2407429e0dc6c47a405f93a032bab384 Mon Sep 17 00:00:00 2001 From: zolizoli Date: Mon, 29 Jun 2026 17:51:09 +0200 Subject: [PATCH] Test hardening: property-based tests + mutation-score gaps closed MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Property-based tests targeting real contracts on the algorithmic core: - backbone: disparity-filter math — alpha(0, degree) == 1, monotonic decrease in norm_weight, node strength == sum of incident edge weights, alpha/ptile/ norm_weight all in [0, 1]; plus exact-value unit tests pinning the formula. - cooccurrence: edge weights sum to 1 (normalisation invariant); detect_collocations invariants (results are n-tuples drawn from the text's contiguous n-grams, bounded by top_n); exact-structure + inclusive-min_weight + default-window tests. - graphs: cosine_similarity_matrix diagonal == 1 and values in [-1, 1]. Mutation testing (mutmut 3.6, dev-only, not in CI): - Add [tool.mutmut] scoped to the math core (backbone/cooccurrence/graphs) and its fast, spaCy-free tests; add mutmut to the dev extra. - Env-gated Hypothesis profile in conftest so mutmut's parallel workers don't trip the differing_executors health check. - Score ~82% (362/442 killed). MUTATION-TESTING.md records the score, the gaps closed, and triage of the remaining (largely equivalent) survivors. Finding (left unfixed, see CHANGES_SUMMARY): detect_collocations propagates NLTK exceptions on degenerate corpora — chi_sq raises ZeroDivisionError, likelihood a math-domain ValueError, on all-identical tokens. Only pmi is robust; the property test uses pmi. uv.lock regenerated for the mutmut dev dep (also picks up the stale 0.1.0->0.1.1). make ci green (132 tests, 94% coverage). Co-Authored-By: Claude Opus 4.8 (1M context) --- .gitignore | 4 + MUTATION-TESTING.md | 87 +++++++++++++ pyproject.toml | 17 +++ tests/conftest.py | 16 +++ tests/test_backbone.py | 59 +++++++++ tests/test_cooccurrence.py | 69 ++++++++++ tests/test_graphs.py | 22 ++++ uv.lock | 249 +++++++++++++++++++++++++++++++++++-- 8 files changed, 516 insertions(+), 7 deletions(-) create mode 100644 MUTATION-TESTING.md diff --git a/.gitignore b/.gitignore index 82d863f..c173019 100644 --- a/.gitignore +++ b/.gitignore @@ -21,5 +21,9 @@ experiments/semantic_explorer/data/graphs/ .coverage .hypothesis/ +# Mutation testing (mutmut) working copy and cache +mutants/ +.mutmut-cache + # Jupyter checkpoints .ipynb_checkpoints/ diff --git a/MUTATION-TESTING.md b/MUTATION-TESTING.md new file mode 100644 index 0000000..89a8cc8 --- /dev/null +++ b/MUTATION-TESTING.md @@ -0,0 +1,87 @@ +# Mutation testing summary + +Tool: [`mutmut`](https://github.com/boxed/mutmut) 3.6 (dev-only; not run in CI). +Run with `KENON_MUTATION=1 uv run mutmut run` (the env var loads a Hypothesis +profile in `tests/conftest.py` that suppresses the `differing_executors` health +check and disables the example database during parallel mutation runs). + +## Scope + +Mutation is scoped to the **math-heavy core** and its fast, spaCy-free tests +(see `[tool.mutmut]` in `pyproject.toml`): + +- mutated: `backbone.py`, `cooccurrence.py`, `graphs.py` +- test selection: `test_backbone.py`, `test_cooccurrence.py`, `test_graphs.py` + +`tokenizer.py`, `embeddings.py`, and `stopwords.py` are **not** mutated — their +tests load spaCy / sklearn and are too slow to run per-mutant. This is a +deliberate cap, not full coverage. + +## Score + +| metric | value | +|--------|------:| +| total mutants | 442 | +| killed | 361 | +| killed by timeout | 1 | +| **survived** | **80** | +| **mutation score** | **~82%** (362 / 442) | + +> Note on reproducibility: the new property-based tests run with the Hypothesis +> example database disabled during mutation (so parallel workers don't collide), +> which means a *different* random example set runs each time. As a result the +> exact survivor count drifts by ~±10 between runs in the property-tested +> functions (`detect_collocations`, `apply_disparity_filter`). The +> deterministic exact-value / exact-structure tests added below are stable kills. + +## Gaps closed this pass + +Tests added specifically to kill surviving mutants: + +- **`disparity_integral` / `get_disparity_significance`** — exact-value unit + tests pinning the formula (`disparity_integral(0.5, 3) == -0.125`, + `get_disparity_significance(0.5, 3) == 0.25`). The range/monotonicity property + tests left arithmetic-operator mutants alive; these kill them. The disparity + formula now has **zero survivors**. +- **`build_cooccurrence_graph`** — an exact-structure test on `["a","b","c"]` + (pins the skip-gram window arithmetic and the 0.5 normalised weights), an + inclusive-`min_weight` test (`>=` vs `>`), and a default-`window`-is-2 test. +- **`apply_disparity_filter`** — the `norm_weight` value is now asserted in + `[0, 1]` (previously only its key presence was checked, so `norm_weight = None` + survived). + +## Remaining survivors (80) — triage + +Most survivors are **equivalent mutants** (no input can distinguish them) or +require contrived inputs for negligible value: + +- **`cosine_similarity_matrix` (10)** — almost all equivalent. The + `np.clip(sim, -1, 1)` is defensive: cosine values are already in `[-1, 1]`, so + mutating the clip bounds changes nothing. sklearn assigns vocabulary indices + alphabetically, so the `key=lambda w: vocab_dict[w]` sort mutating to `key=None` + yields identical order. `.astype(None)` defaults to float64. +- **`apply_disparity_filter` (12) / `extract_backbone` (13)** — largely + unreachable branches: `data.get("weight", )` fallbacks never fire + (edges always carry weights), and `strength <= 0` guards never fire (strengths + are positive for connected nodes). +- **`build_semantic_graph` (24)** — concentrated in the optional `k_neighbors` + path and threshold bookkeeping; many are behaviour-equivalent on realistic + inputs. (This function is also flagged in `PRE-MORTEM.md` for a silent-no-op + fragility.) +- **`build_cooccurrence_graph` (6)** — `total_pairs == 1` is unreachable + (co-occurrence events are always counted symmetrically, so the total is even); + the remaining window-arithmetic mutants produce identical output on the + symmetric test input. +- **`detect_collocations` (11)** — mutations inside the NLTK metric dispatch; + killed inconsistently by the random property examples (see reproducibility + note). +- **`save_graph` / `load_graph` (2 each)** — format-string dispatch details. + +## Finding surfaced by this pass + +While hardening `detect_collocations`, the property tests revealed that it +**propagates NLTK exceptions on degenerate corpora**: `metric="chi_sq"` raises +`ZeroDivisionError` and `metric="likelihood"` raises a math-domain `ValueError` +on all-identical token input (e.g. `["a"] * 12`). Only `"pmi"` is robust. The +property test uses `"pmi"` to stay green; the crash is left unfixed (it changes +public behaviour) and recorded in `CHANGES_SUMMARY.md` for a human decision. diff --git a/pyproject.toml b/pyproject.toml index bb98682..f60fe25 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -44,6 +44,7 @@ dev = [ "hypothesis>=6.100", "ruff>=0.4", "ty", + "mutmut>=3", # experiment deps -- not part of the published package "datasets>=2.18", "huggingface-hub>=0.22", @@ -82,4 +83,20 @@ convention = "google" addopts = "--doctest-modules --tb=short" testpaths = ["src", "tests"] +# Mutation testing (dev-only, not run in CI). Scoped to the math-heavy core and +# its fast, spaCy-free test files. Run with `uv run mutmut run`. +[tool.mutmut] +source_paths = ["src/kenon"] +only_mutate = [ + "src/kenon/backbone.py", + "src/kenon/cooccurrence.py", + "src/kenon/graphs.py", +] +pytest_add_cli_args_test_selection = [ + "tests/test_backbone.py", + "tests/test_cooccurrence.py", + "tests/test_graphs.py", +] +pytest_add_cli_args = ["-p", "no:cacheprovider"] + [tool.ty] diff --git a/tests/conftest.py b/tests/conftest.py index d5eeb07..e44fa14 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,6 +1,22 @@ """Shared pytest fixtures for kenon tests.""" +import os + import pytest +from hypothesis import HealthCheck, settings + +# Mutation testing (mutmut) runs the same property test from multiple forked +# workers, which trips Hypothesis's ``differing_executors`` health check. This +# env-gated profile suppresses that check (and the shared example database) only +# during mutation runs — the normal test suite is unaffected. +settings.register_profile( + "mutation", + suppress_health_check=[HealthCheck.differing_executors], + database=None, + deadline=None, +) +if os.environ.get("KENON_MUTATION"): + settings.load_profile("mutation") @pytest.fixture diff --git a/tests/test_backbone.py b/tests/test_backbone.py index 67deef6..d466fbc 100644 --- a/tests/test_backbone.py +++ b/tests/test_backbone.py @@ -3,6 +3,7 @@ import copy import networkx as nx +import pytest from hypothesis import given, settings from hypothesis import strategies as st @@ -12,6 +13,7 @@ extract_backbone, get_disparity_significance, ) +from tests.strategies import weighted_graph def _make_weighted_graph(n_nodes: int = 6) -> nx.Graph: @@ -44,6 +46,12 @@ def test_different_inputs(self) -> None: v2 = disparity_integral(0.7, 4.0) assert v1 != v2 + def test_exact_value(self) -> None: + # ((1-0.5)^3) / ((3-1) * (0.5-1)) = 0.125 / (2 * -0.5) = -0.125 + assert disparity_integral(0.5, 3.0) == pytest.approx(-0.125) + # ((1-0)^3) / ((3-1) * (0-1)) = 1 / -2 = -0.5 + assert disparity_integral(0.0, 3.0) == pytest.approx(-0.5) + class TestGetDisparitySignificance: """Unit tests for get_disparity_significance.""" @@ -60,6 +68,10 @@ def test_in_range(self) -> None: alpha = get_disparity_significance(0.5, 3.0) assert 0.0 <= alpha <= 1.0 + def test_exact_value(self) -> None: + # 1 - (3-1) * (I(0.5,3) - I(0,3)) = 1 - 2 * (-0.125 - -0.5) = 1 - 0.75 = 0.25 + assert get_disparity_significance(0.5, 3.0) == pytest.approx(0.25) + class TestApplyDisparityFilter: """Unit tests for apply_disparity_filter.""" @@ -172,3 +184,50 @@ def test_significance_in_unit_interval( ) -> None: alpha = get_disparity_significance(norm_weight, degree) assert 0.0 <= alpha <= 1.0 + + +class TestDisparityMath: + """Property-based tests asserting the documented disparity-filter contracts.""" + + def test_zero_norm_weight_is_maximally_insignificant(self) -> None: + # A normalised weight of 0 carries no significance: alpha is exactly 1.0. + for degree in (2.0, 3.0, 10.0, 100.0, 1000.0): + assert get_disparity_significance(0.0, degree) == 1.0 + + @settings(max_examples=200) + @given( + st.floats(min_value=0.0, max_value=0.9999, allow_nan=False), + st.floats(min_value=0.0, max_value=0.9999, allow_nan=False), + st.floats(min_value=2.0, max_value=1000.0, allow_nan=False), + ) + def test_significance_monotonic_in_norm_weight( + self, n1: float, n2: float, degree: float + ) -> None: + # A stronger normalised edge is MORE significant, i.e. has a LOWER alpha. + lo, hi = sorted((n1, n2)) + assert get_disparity_significance(lo, degree) >= ( + get_disparity_significance(hi, degree) - 1e-12 + ) + + @settings(max_examples=50, deadline=5000) + @given(weighted_graph()) + def test_strength_equals_incident_weight_sum(self, g: nx.Graph) -> None: + if g.number_of_edges() == 0: + assert apply_disparity_filter(g) == [] + return + apply_disparity_filter(g) + for node in g.nodes(): + incident = sum(d["weight"] for _, _, d in g.edges(node, data=True)) + assert abs(g.nodes[node]["strength"] - incident) < 1e-9 + + @settings(max_examples=50, deadline=5000) + @given(weighted_graph()) + def test_alpha_ptile_and_norm_weight_in_unit_interval(self, g: nx.Graph) -> None: + alphas = apply_disparity_filter(g) + for a in alphas: + assert 0.0 <= a <= 1.0 + for _u, _v, data in g.edges(data=True): + assert 0.0 <= data["alpha"] <= 1.0 + assert 0.0 <= data["alpha_ptile"] <= 1.0 + # Contract: every edge gets a numeric norm_weight (weight / strength). + assert 0.0 <= data["norm_weight"] <= 1.0 diff --git a/tests/test_cooccurrence.py b/tests/test_cooccurrence.py index ec938be..787f7bd 100644 --- a/tests/test_cooccurrence.py +++ b/tests/test_cooccurrence.py @@ -49,6 +49,27 @@ def test_single_token(self) -> None: g = build_cooccurrence_graph(["hello"], window=1) assert g.number_of_edges() == 0 + def test_exact_structure_and_weights(self) -> None: + # ["a", "b", "c"] with window=1: each adjacent pair co-occurs twice + # (once from each direction), giving normalised weights of 0.5 each, and + # NO a-c edge (they are 2 apart). Pins the skip-gram window arithmetic. + g = build_cooccurrence_graph(["a", "b", "c"], window=1) + assert set(g.nodes()) == {"a", "b", "c"} + assert g["a"]["b"]["weight"] == pytest.approx(0.5) + assert g["b"]["c"]["weight"] == pytest.approx(0.5) + assert not g.has_edge("a", "c") + + def test_min_weight_is_inclusive(self) -> None: + # Both edges have weight exactly 0.5; min_weight=0.5 must keep them (>=). + g = build_cooccurrence_graph(["a", "b", "c"], window=1, min_weight=0.5) + assert g.number_of_edges() == 2 + + def test_default_window_is_two(self) -> None: + # Default window is 2: tokens 2 apart co-occur, tokens 3 apart do not. + g = build_cooccurrence_graph(["a", "b", "c", "d"]) + assert g.has_edge("a", "c") + assert not g.has_edge("a", "d") + class TestDetectCollocations: """Unit tests for detect_collocations.""" @@ -129,3 +150,51 @@ def test_min_weight_respected(self, tokens: list[str], min_w: float) -> None: g = build_cooccurrence_graph(tokens, window=2, min_weight=min_w) for _u, _v, data in g.edges(data=True): assert data["weight"] >= min_w + + @settings(max_examples=50, deadline=5000) + @given(token_list, st.integers(min_value=1, max_value=5)) + def test_weights_sum_to_one(self, tokens: list[str], window: int) -> None: + # Edge weights are normalised co-occurrence frequencies: with no + # min_weight cut, they must sum to 1.0 over the whole graph. + g = build_cooccurrence_graph(tokens, window=window, min_weight=0.0) + if g.number_of_edges() == 0: + return + total = sum(data["weight"] for _u, _v, data in g.edges(data=True)) + assert abs(total - 1.0) < 1e-9 + + +# Tokens drawn from a tiny alphabet so n-grams actually repeat and collocations +# can be found (exercising the scoring path, not just the empty-result path). +_repeating_tokens = st.lists( + st.sampled_from(["a", "b", "c", "d", "e", "f"]), + min_size=12, + max_size=80, +) + + +class TestCollocationProperties: + """Property-based tests for detect_collocations invariants. + + Uses the ``pmi`` metric only: the structural invariants checked here are + metric-independent, and NLTK's ``chi_sq`` / ``likelihood`` scorers raise + (ZeroDivisionError / math-domain ValueError) on degenerate corpora such as + all-identical tokens, which detect_collocations does not currently guard. + """ + + @settings(max_examples=60, deadline=5000) + @given( + _repeating_tokens, + st.sampled_from([2, 3]), + st.integers(min_value=1, max_value=10), + ) + def test_collocation_invariants( + self, tokens: list[str], n: int, top_n: int + ) -> None: + result = detect_collocations(tokens, n=n, metric="pmi", top_n=top_n, min_freq=2) + contiguous_ngrams = { + tuple(tokens[i : i + n]) for i in range(len(tokens) - n + 1) + } + # At most top_n results, each an n-tuple drawn from the text's n-grams. + assert len(result) <= top_n + assert all(len(t) == n for t in result) + assert all(t in contiguous_ngrams for t in result) diff --git a/tests/test_graphs.py b/tests/test_graphs.py index 01882c2..f587d23 100644 --- a/tests/test_graphs.py +++ b/tests/test_graphs.py @@ -80,6 +80,19 @@ def test_symmetric(self, sample_corpus: list[str]) -> None: sim, _ = cosine_similarity_matrix(emb, sample_corpus) np.testing.assert_allclose(sim, sim.T, atol=1e-10) + def test_diagonal_is_one(self, sample_corpus: list[str]) -> None: + # Contract: self-similarity is 1.0 on the diagonal. + emb = TfidfEmbedder() + sim, vocab = cosine_similarity_matrix(emb, sample_corpus) + np.testing.assert_allclose(np.diag(sim), np.ones(len(vocab)), atol=1e-9) + + def test_values_in_unit_range(self, sample_corpus: list[str]) -> None: + # Contract: all cosine values lie in [-1, 1]. + emb = TfidfEmbedder() + sim, _ = cosine_similarity_matrix(emb, sample_corpus) + assert sim.min() >= -1.0 - 1e-9 + assert sim.max() <= 1.0 + 1e-9 + class TestSaveLoadGraph: """Unit tests for save_graph and load_graph.""" @@ -151,6 +164,15 @@ def test_cosine_matrix_symmetric(self, corpus: list[str]) -> None: sim, _ = cosine_similarity_matrix(emb, corpus) np.testing.assert_allclose(sim, sim.T, atol=1e-10) + @settings(max_examples=10, deadline=10000) + @given(small_corpus) + def test_cosine_matrix_diagonal_is_one(self, corpus: list[str]) -> None: + emb = CountVectorizerEmbedder() + sim, vocab = cosine_similarity_matrix(emb, corpus) + if len(vocab) == 0: + return + np.testing.assert_allclose(np.diag(sim), np.ones(len(vocab)), atol=1e-9) + @settings(max_examples=10, deadline=10000) @given(small_corpus) def test_higher_threshold_fewer_edges(self, corpus: list[str]) -> None: diff --git a/uv.lock b/uv.lock index 943ab77..80ebd31 100644 --- a/uv.lock +++ b/uv.lock @@ -5,11 +5,14 @@ resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", "python_full_version >= '3.14' and sys_platform == 'emscripten'", "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", "python_full_version < '3.12' and sys_platform == 'win32'", - "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'emscripten'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", "python_full_version < '3.12' and sys_platform == 'emscripten'", - "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", "python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'", ] @@ -1358,9 +1361,12 @@ resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", "python_full_version >= '3.14' and sys_platform == 'emscripten'", "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten'", + "python_full_version == '3.12.*' and sys_platform == 'emscripten'", + "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", ] dependencies = [ { name = "colorama", marker = "python_full_version >= '3.12' and sys_platform == 'win32'" }, @@ -1713,7 +1719,7 @@ wheels = [ [[package]] name = "kenon" -version = "0.1.0" +version = "0.1.1" source = { editable = "." } dependencies = [ { name = "chronowords" }, @@ -1734,6 +1740,7 @@ dev = [ { name = "ipykernel" }, { name = "jupyter" }, { name = "matplotlib" }, + { name = "mutmut" }, { name = "pytest" }, { name = "pytest-cov" }, { name = "ruff" }, @@ -1757,6 +1764,7 @@ requires-dist = [ { name = "matplotlib", marker = "extra == 'dev'", specifier = ">=3.8" }, { name = "mkdocs-material", marker = "extra == 'docs'" }, { name = "mkdocstrings", extras = ["python"], marker = "extra == 'docs'" }, + { name = "mutmut", marker = "extra == 'dev'", specifier = ">=3" }, { name = "networkx", specifier = ">=3.3" }, { name = "nltk", specifier = ">=3.8" }, { name = "numpy", specifier = ">=1.26" }, @@ -1888,6 +1896,78 @@ wheels = [ { url = 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