[{"data":1,"prerenderedAt":1105},["ShallowReactive",2],{"page-\u002Fproperty-based-fuzz-testing-strategies\u002Fhypothesis-framework-fundamentals\u002Freducing-hypothesis-test-execution-time\u002F":3},{"id":4,"title":5,"body":6,"description":1098,"extension":1099,"meta":1100,"navigation":230,"path":1101,"seo":1102,"stem":1103,"__hash__":1104},"content\u002Fproperty-based-fuzz-testing-strategies\u002Fhypothesis-framework-fundamentals\u002Freducing-hypothesis-test-execution-time\u002Findex.md","Reducing hypothesis test execution time",{"type":7,"value":8,"toc":1089},"minimark",[9,13,32,37,48,55,96,99,125,150,170,174,189,209,260,271,372,396,418,422,429,440,509,523,543,547,550,567,633,642,660,664,675,885,902,913,917,932,939,950,957,963,1005,1009,1025,1040,1059,1071,1085],[10,11,5],"h1",{"id":12},"reducing-hypothesis-test-execution-time",[14,15,16,17,21,22,25,26,31],"p",{},"Property-based testing fundamentally shifts the verification paradigm from static, handcrafted inputs to exhaustive, algorithmically generated data spaces. This paradigm shift introduces a well-known performance paradox: the pursuit of comprehensive edge-case coverage inherently increases execution overhead. When Hypothesis-driven suites regress into multi-hour CI runs, developers often resort to blunt instruments like ",[18,19,20],"code",{},"max_examples=10"," or ",[18,23,24],{},"deadline=None",", which artificially accelerate execution while silently degrading failure detection capabilities. A diagnostic-first methodology is required to isolate latency sources without sacrificing the mathematical guarantees of property-based testing. Speed optimizations must never compromise failure reproducibility or shrinking fidelity. Within the broader ",[27,28,30],"a",{"href":29},"\u002Fproperty-based-fuzz-testing-strategies\u002F","Property-Based & Fuzz Testing Strategies"," ecosystem, performance tuning is not about disabling Hypothesis features, but about aligning strategy complexity, phase execution, and infrastructure caching with the actual computational boundaries of your system under test.",[33,34,36],"h2",{"id":35},"diagnosing-execution-bottlenecks-with-precision","Diagnosing Execution Bottlenecks with Precision",[14,38,39,40,43,44,47],{},"Blindly adjusting ",[18,41,42],{},"@settings"," parameters without empirical profiling guarantees suboptimal outcomes. Hypothesis execution comprises three distinct computational phases: data generation, test function evaluation, and shrinking. Each phase exhibits different latency characteristics and requires targeted instrumentation. The most reliable diagnostic workflow begins with ",[18,45,46],{},"pytest-profiling"," combined with Hypothesis's native statistics reporting.",[14,49,50,51,54],{},"Configure your ",[18,52,53],{},"pytest.ini"," to enable granular timing isolation:",[56,57,62],"pre",{"className":58,"code":59,"language":60,"meta":61,"style":61},"language-ini shiki shiki-themes github-light github-dark","[pytest]\naddopts = \n --profile-svg\n --hypothesis-show-statistics\n -v\n","ini","",[18,63,64,72,78,84,90],{"__ignoreMap":61},[65,66,69],"span",{"class":67,"line":68},"line",1,[65,70,71],{},"[pytest]\n",[65,73,75],{"class":67,"line":74},2,[65,76,77],{},"addopts = \n",[65,79,81],{"class":67,"line":80},3,[65,82,83],{}," --profile-svg\n",[65,85,87],{"class":67,"line":86},4,[65,88,89],{}," --hypothesis-show-statistics\n",[65,91,93],{"class":67,"line":92},5,[65,94,95],{}," -v\n",[14,97,98],{},"Execute the targeted suite with:",[56,100,104],{"className":101,"code":102,"language":103,"meta":61,"style":61},"language-bash shiki shiki-themes github-light github-dark","pytest --profile-svg -k 'test_property' --hypothesis-show-statistics\n","bash",[18,105,106],{"__ignoreMap":61},[65,107,108,112,116,119,123],{"class":67,"line":68},[65,109,111],{"class":110},"sScJk","pytest",[65,113,115],{"class":114},"sj4cs"," --profile-svg",[65,117,118],{"class":114}," -k",[65,120,122],{"class":121},"sZZnC"," 'test_property'",[65,124,89],{"class":114},[14,126,127,128,131,132,135,136,139,140,143,144,146,147,149],{},"The generated ",[18,129,130],{},"prof\u002Fcombined.svg"," flame graph immediately reveals whether CPU cycles are consumed by strategy construction, assertion logic, or Hypothesis's internal shrinking engine. Simultaneously, the ",[18,133,134],{},"--hypothesis-show-statistics"," output provides phase-level breakdowns. Pay close attention to the ",[18,137,138],{},"Generate"," vs ",[18,141,142],{},"Shrink"," time ratios. If ",[18,145,138],{}," dominates, your strategies are producing excessively large or deeply nested objects. If ",[18,148,142],{}," dominates, your test function contains expensive side effects or complex validation logic that executes repeatedly during minimization.",[14,151,152,153,156,157,160,161,165,166,169],{},"For deeper isolation, leverage ",[18,154,155],{},"hypothesis.internal.conjecture.data.DataObserver"," metrics or wrap the test body with ",[18,158,159],{},"time.perf_counter()",". Understanding how the underlying Conjecture engine allocates bytes and tracks phase transitions is essential. Foundational concepts from ",[27,162,164],{"href":163},"\u002Fproperty-based-fuzz-testing-strategies\u002Fhypothesis-framework-fundamentals\u002F","Hypothesis Framework Fundamentals"," directly inform bottleneck isolation, particularly around how ",[18,167,168],{},"DataObserver"," tracks example generation throughput and how phase timing correlates with strategy complexity bounds. When profiling reveals that a single test consumes 80% of the suite's runtime, isolate it in a dedicated module and apply the optimization strategies detailed below. Never optimize globally; always optimize per-property based on empirical phase timing.",[33,171,173],{"id":172},"optimizing-strategy-generation-data-complexity","Optimizing Strategy Generation & Data Complexity",[14,175,176,177,180,181,184,185,188],{},"Strategy generation overhead scales non-linearly with structural complexity. Unbounded strategies like ",[18,178,179],{},"st.text()",", ",[18,182,183],{},"st.lists(st.text())",", or ",[18,186,187],{},"st.dictionaries()"," without explicit constraints will rapidly exhaust memory and CPU during both generation and shrinking. The first line of defense is explicit complexity bounding.",[14,190,191,192,180,195,180,198,201,202,205,206,208],{},"Apply ",[18,193,194],{},"max_size",[18,196,197],{},"min_value",[18,199,200],{},"max_value",", and ",[18,203,204],{},"allow_nan=False"," aggressively. For collections, ",[18,207,194],{}," directly caps the number of elements Hypothesis will attempt to generate. For numerical types, bounding ranges prevents floating-point edge cases from triggering expensive downstream arithmetic or serialization paths.",[56,210,214],{"className":211,"code":212,"language":213,"meta":61,"style":61},"language-python shiki shiki-themes github-light github-dark","from hypothesis import given, settings\nimport hypothesis.strategies as st\n\n@given(st.lists(st.text(max_size=20), max_size=10))\n@settings(max_examples=200)\ndef test_bounded_list_processing(data: list[str]) -> None:\n # Processing logic remains stable due to strict bounds\n assert len(\"\".join(data)) \u003C= 200\n","python",[18,215,216,221,226,232,237,242,248,254],{"__ignoreMap":61},[65,217,218],{"class":67,"line":68},[65,219,220],{},"from hypothesis import given, settings\n",[65,222,223],{"class":67,"line":74},[65,224,225],{},"import hypothesis.strategies as st\n",[65,227,228],{"class":67,"line":80},[65,229,231],{"emptyLinePlaceholder":230},true,"\n",[65,233,234],{"class":67,"line":86},[65,235,236],{},"@given(st.lists(st.text(max_size=20), max_size=10))\n",[65,238,239],{"class":67,"line":92},[65,240,241],{},"@settings(max_examples=200)\n",[65,243,245],{"class":67,"line":244},6,[65,246,247],{},"def test_bounded_list_processing(data: list[str]) -> None:\n",[65,249,251],{"class":67,"line":250},7,[65,252,253],{}," # Processing logic remains stable due to strict bounds\n",[65,255,257],{"class":67,"line":256},8,[65,258,259],{}," assert len(\"\".join(data)) \u003C= 200\n",[14,261,262,263,266,267,270],{},"Recursive strategies present the highest risk of combinatorial explosion. Without explicit depth limits, ",[18,264,265],{},"st.recursive()"," will attempt to generate trees or graphs that exceed stack limits or trigger pathological shrinking loops. Always constrain ",[18,268,269],{},"max_leaves"," and implement explicit depth tracking.",[56,272,274],{"className":211,"code":273,"language":213,"meta":61,"style":61},"from hypothesis import given, settings\nimport hypothesis.strategies as st\n\n@st.composite\ndef bounded_tree(draw, max_depth: int = 3):\n \"\"\"Prevents exponential blowup by tracking recursion depth explicitly.\"\"\"\n depth = draw(st.integers(0, max_depth))\n if depth == 0:\n return None\n left = draw(bounded_tree(max_depth=depth - 1))\n right = draw(bounded_tree(max_depth=depth - 1))\n return {\"value\": draw(st.integers(-100, 100)), \"left\": left, \"right\": right}\n\n@given(bounded_tree(max_depth=4))\n@settings(max_examples=150)\ndef test_tree_traversal(tree: dict | None) -> None:\n # Traversal logic executes predictably\n pass\n",[18,275,276,280,284,288,293,298,303,308,313,319,325,331,337,342,348,354,360,366],{"__ignoreMap":61},[65,277,278],{"class":67,"line":68},[65,279,220],{},[65,281,282],{"class":67,"line":74},[65,283,225],{},[65,285,286],{"class":67,"line":80},[65,287,231],{"emptyLinePlaceholder":230},[65,289,290],{"class":67,"line":86},[65,291,292],{},"@st.composite\n",[65,294,295],{"class":67,"line":92},[65,296,297],{},"def bounded_tree(draw, max_depth: int = 3):\n",[65,299,300],{"class":67,"line":244},[65,301,302],{}," \"\"\"Prevents exponential blowup by tracking recursion depth explicitly.\"\"\"\n",[65,304,305],{"class":67,"line":250},[65,306,307],{}," depth = draw(st.integers(0, max_depth))\n",[65,309,310],{"class":67,"line":256},[65,311,312],{}," if depth == 0:\n",[65,314,316],{"class":67,"line":315},9,[65,317,318],{}," return None\n",[65,320,322],{"class":67,"line":321},10,[65,323,324],{}," left = draw(bounded_tree(max_depth=depth - 1))\n",[65,326,328],{"class":67,"line":327},11,[65,329,330],{}," right = draw(bounded_tree(max_depth=depth - 1))\n",[65,332,334],{"class":67,"line":333},12,[65,335,336],{}," return {\"value\": draw(st.integers(-100, 100)), \"left\": left, \"right\": right}\n",[65,338,340],{"class":67,"line":339},13,[65,341,231],{"emptyLinePlaceholder":230},[65,343,345],{"class":67,"line":344},14,[65,346,347],{},"@given(bounded_tree(max_depth=4))\n",[65,349,351],{"class":67,"line":350},15,[65,352,353],{},"@settings(max_examples=150)\n",[65,355,357],{"class":67,"line":356},16,[65,358,359],{},"def test_tree_traversal(tree: dict | None) -> None:\n",[65,361,363],{"class":67,"line":362},17,[65,364,365],{}," # Traversal logic executes predictably\n",[65,367,369],{"class":67,"line":368},18,[65,370,371],{}," pass\n",[14,373,374,375,378,379,381,382,21,385,388,389,391,392,395],{},"Lazy evaluation and ",[18,376,377],{},"@st.composite"," trade-offs also impact performance. While ",[18,380,377],{}," provides imperative control over generation, it incurs interpreter overhead compared to declarative strategies like ",[18,383,384],{},"st.builds()",[18,386,387],{},"st.from_type()",". Reserve ",[18,390,377],{}," for scenarios requiring conditional branching, cross-field dependencies, or stateful generation. For flat data structures, prefer ",[18,393,394],{},"st.builds(Model, field=st.integers(min_value=0, max_value=10))"," to leverage Hypothesis's optimized internal generators.",[14,397,398,399,402,403,21,406,409,410,413,414,417],{},"Adjust ",[18,400,401],{},"@settings(max_examples=...)"," based on property criticality, not arbitrary defaults. Core invariants warrant ",[18,404,405],{},"max_examples=500",[18,407,408],{},"1000",", while peripheral utilities may safely operate at ",[18,411,412],{},"100",". The ",[18,415,416],{},"deadline"," parameter should reflect actual algorithmic complexity, not wall-clock tolerance. Misinterpreting deadline warnings as pure test slowness instead of algorithmic complexity issues is a frequent anti-pattern. If a test consistently breaches the default 200ms deadline, profile the test logic before increasing the threshold.",[33,419,421],{"id":420},"leveraging-caching-database-strategies","Leveraging Caching & Database Strategies",[14,423,424,425,428],{},"Hypothesis's example database is a deterministic persistence layer that stores failing examples across test runs. It is the single most impactful mechanism for reducing CI execution time. When a test fails locally, Hypothesis serializes the minimal failing input to ",[18,426,427],{},".hypothesis\u002Fexamples\u002F",". On subsequent runs, it prioritizes these cached examples before generating new ones, ensuring immediate regression detection and eliminating redundant exploration of already-verified spaces.",[14,430,431,432,435,436,439],{},"In CI environments, the ",[18,433,434],{},".hypothesis"," directory is frequently ephemeral, forcing full regeneration and shrinking on every pipeline execution. To mitigate this, configure ",[18,437,438],{},"DirectoryBasedExampleDatabase"," with a standardized, cacheable path:",[56,441,443],{"className":211,"code":442,"language":213,"meta":61,"style":61},"import os\nfrom hypothesis import settings\nfrom hypothesis.database import DirectoryBasedExampleDatabase\n\n# Standardize database location for CI artifact caching\ndb_path = os.path.join(os.getenv(\"CI_CACHE_DIR\", \".hypothesis\"), \"examples\")\nsettings.register_profile(\n \"ci\",\n database=DirectoryBasedExampleDatabase(db_path),\n deadline=None, # CI runners often have variable I\u002FO latency\n max_examples=300\n)\nsettings.load_profile(\"ci\")\n",[18,444,445,450,455,460,464,469,474,479,484,489,494,499,504],{"__ignoreMap":61},[65,446,447],{"class":67,"line":68},[65,448,449],{},"import os\n",[65,451,452],{"class":67,"line":74},[65,453,454],{},"from hypothesis import settings\n",[65,456,457],{"class":67,"line":80},[65,458,459],{},"from hypothesis.database import DirectoryBasedExampleDatabase\n",[65,461,462],{"class":67,"line":86},[65,463,231],{"emptyLinePlaceholder":230},[65,465,466],{"class":67,"line":92},[65,467,468],{},"# Standardize database location for CI artifact caching\n",[65,470,471],{"class":67,"line":244},[65,472,473],{},"db_path = os.path.join(os.getenv(\"CI_CACHE_DIR\", \".hypothesis\"), \"examples\")\n",[65,475,476],{"class":67,"line":250},[65,477,478],{},"settings.register_profile(\n",[65,480,481],{"class":67,"line":256},[65,482,483],{}," \"ci\",\n",[65,485,486],{"class":67,"line":315},[65,487,488],{}," database=DirectoryBasedExampleDatabase(db_path),\n",[65,490,491],{"class":67,"line":321},[65,492,493],{}," deadline=None, # CI runners often have variable I\u002FO latency\n",[65,495,496],{"class":67,"line":327},[65,497,498],{}," max_examples=300\n",[65,500,501],{"class":67,"line":333},[65,502,503],{},")\n",[65,505,506],{"class":67,"line":339},[65,507,508],{},"settings.load_profile(\"ci\")\n",[14,510,511,512,514,515,518,519,522],{},"Manage the ",[18,513,434],{}," directory lifecycle rigorously. Stale examples accumulate over months and can skew generation toward outdated edge cases. Implement a weekly pruning job or use ",[18,516,517],{},"hypothesis.extra.pytestplugin"," hooks to clear databases older than a defined retention period. For ephemeral, exploratory fuzzing runs, ",[18,520,521],{},"@settings(database=None)"," is appropriate, but never disable the database permanently in production pipelines.",[14,524,525,526,529,530,532,533,535,536,21,539,542],{},"When integrating with ",[18,527,528],{},"pytest-xdist",", database contention becomes a critical failure mode. Multiple workers writing to the same SQLite-backed database triggers locking exceptions and corrupts example serialization. The ",[18,531,517],{}," automatically handles worker isolation by appending a unique suffix to the database path per worker. Ensure your CI configuration respects this isolation pattern and restores the parent ",[18,534,434],{}," directory before test execution. Deterministic seed management via ",[18,537,538],{},"HYPOTHESIS_PROFILE",[18,540,541],{},"@settings(database=...)"," guarantees that parallel runs remain reproducible while maintaining cache integrity.",[33,544,546],{"id":545},"shrinking-phase-optimization-early-exits","Shrinking Phase Optimization & Early Exits",[14,548,549],{},"The shrinking phase is computationally expensive because it repeatedly executes the test function with progressively minimized inputs until the failure condition disappears. Pathological shrinking occurs when the test function contains expensive I\u002FO, heavy cryptographic operations, or complex validation logic that executes on every shrink attempt.",[14,551,552,553,556,557,560,561,563,564,566],{},"The most effective mitigation is early-exit optimization using ",[18,554,555],{},"assume()"," instead of ",[18,558,559],{},"filter()",". ",[18,562,559],{}," generates a complete example, evaluates the predicate, and discards the entire object if it fails. This triggers repeated generation cycles. ",[18,565,555],{}," integrates with Hypothesis's internal byte-stream allocation, allowing the engine to backtrack and adjust generation parameters immediately upon rejection.",[56,568,570],{"className":211,"code":569,"language":213,"meta":61,"style":61},"from hypothesis import given, assume\nimport hypothesis.strategies as st\n\n# Inefficient: filter() forces full generation and discard cycles\n@given(st.integers().filter(lambda x: x % 2 == 0))\ndef test_slow_filter(x: int) -> None:\n assert x \u002F\u002F 2 == x \u002F 2\n\n# Optimized: assume() triggers early rejection at the byte level\n@given(st.integers())\ndef test_fast_assume(x: int) -> None:\n assume(x % 2 == 0)\n assert x \u002F\u002F 2 == x \u002F 2\n",[18,571,572,577,581,585,590,595,600,605,609,614,619,624,629],{"__ignoreMap":61},[65,573,574],{"class":67,"line":68},[65,575,576],{},"from hypothesis import given, assume\n",[65,578,579],{"class":67,"line":74},[65,580,225],{},[65,582,583],{"class":67,"line":80},[65,584,231],{"emptyLinePlaceholder":230},[65,586,587],{"class":67,"line":86},[65,588,589],{},"# Inefficient: filter() forces full generation and discard cycles\n",[65,591,592],{"class":67,"line":92},[65,593,594],{},"@given(st.integers().filter(lambda x: x % 2 == 0))\n",[65,596,597],{"class":67,"line":244},[65,598,599],{},"def test_slow_filter(x: int) -> None:\n",[65,601,602],{"class":67,"line":250},[65,603,604],{}," assert x \u002F\u002F 2 == x \u002F 2\n",[65,606,607],{"class":67,"line":256},[65,608,231],{"emptyLinePlaceholder":230},[65,610,611],{"class":67,"line":315},[65,612,613],{},"# Optimized: assume() triggers early rejection at the byte level\n",[65,615,616],{"class":67,"line":321},[65,617,618],{},"@given(st.integers())\n",[65,620,621],{"class":67,"line":327},[65,622,623],{},"def test_fast_assume(x: int) -> None:\n",[65,625,626],{"class":67,"line":333},[65,627,628],{}," assume(x % 2 == 0)\n",[65,630,631],{"class":67,"line":339},[65,632,604],{},[14,634,635,636,638,639,641],{},"Benchmarks consistently demonstrate that ",[18,637,555],{}," reduces generation overhead by 60-80% for constrained input spaces. Combine ",[18,640,555],{}," with early assertion guards in your test logic. If a property requires a specific state, validate it immediately before expensive operations.",[14,643,644,645,648,649,651,652,655,656,659],{},"For pathological shrinking loops, temporarily restrict phases using ",[18,646,647],{},"@settings(phases=[Phase.generate, Phase.shrink])"," or implement custom shrink predicates via ",[18,650,517],{},". If shrinking consistently times out, isolate the failing example, reproduce it deterministically, and refactor the test to eliminate side effects. Never disable ",[18,653,654],{},"Phase.shrink"," permanently; doing so sacrifices minimal repro generation, transforming actionable failures into opaque stack traces. Use ",[18,657,658],{},"@settings(phases=[Phase.generate])"," exclusively for exploratory smoke tests, and re-enable shrinking for all production pipelines.",[33,661,663],{"id":662},"cicd-pipeline-integration-parallelization","CI\u002FCD Pipeline Integration & Parallelization",[14,665,666,667,670,671,674],{},"Integrating Hypothesis into CI\u002FCD pipelines requires careful orchestration of caching, parallelization, and environment variable overrides. GitHub Actions, GitLab CI, and ",[18,668,669],{},"tox"," all support artifact caching, but Hypothesis requires explicit path mapping to preserve the ",[18,672,673],{},".hypothesis\u002Fexamples"," directory across workflow runs.",[56,676,680],{"className":677,"code":678,"language":679,"meta":61,"style":61},"language-yaml shiki shiki-themes github-light github-dark","# .github\u002Fworkflows\u002Ftest.yml\nname: Test Suite\non: [push, pull_request]\n\njobs:\n test:\n runs-on: ubuntu-latest\n steps:\n - uses: actions\u002Fcheckout@v4\n - uses: actions\u002Fsetup-python@v5\n with:\n python-version: \"3.11\"\n - name: Cache Hypothesis Database\n uses: actions\u002Fcache@v3\n with:\n path: .hypothesis\u002Fexamples\n key: hypothesis-db-${{ runner.os }}-${{ hashFiles('**\u002Frequirements.txt') }}\n restore-keys: |\n hypothesis-db-${{ runner.os }}-\n - run: pip install -r requirements.txt pytest pytest-xdist hypothesis\n - run: pytest -n auto --hypothesis-profile ci\n env:\n HYPOTHESIS_PROFILE: ci\n","yaml",[18,681,682,688,701,720,724,732,739,749,756,769,780,787,797,808,818,824,834,844,855,861,867,873,879],{"__ignoreMap":61},[65,683,684],{"class":67,"line":68},[65,685,687],{"class":686},"sJ8bj","# .github\u002Fworkflows\u002Ftest.yml\n",[65,689,690,694,698],{"class":67,"line":74},[65,691,693],{"class":692},"s9eBZ","name",[65,695,697],{"class":696},"sVt8B",": ",[65,699,700],{"class":121},"Test Suite\n",[65,702,703,706,709,712,714,717],{"class":67,"line":80},[65,704,705],{"class":114},"on",[65,707,708],{"class":696},": [",[65,710,711],{"class":121},"push",[65,713,180],{"class":696},[65,715,716],{"class":121},"pull_request",[65,718,719],{"class":696},"]\n",[65,721,722],{"class":67,"line":86},[65,723,231],{"emptyLinePlaceholder":230},[65,725,726,729],{"class":67,"line":92},[65,727,728],{"class":692},"jobs",[65,730,731],{"class":696},":\n",[65,733,734,737],{"class":67,"line":244},[65,735,736],{"class":692}," test",[65,738,731],{"class":696},[65,740,741,744,746],{"class":67,"line":250},[65,742,743],{"class":692}," runs-on",[65,745,697],{"class":696},[65,747,748],{"class":121},"ubuntu-latest\n",[65,750,751,754],{"class":67,"line":256},[65,752,753],{"class":692}," steps",[65,755,731],{"class":696},[65,757,758,761,764,766],{"class":67,"line":315},[65,759,760],{"class":696}," - ",[65,762,763],{"class":692},"uses",[65,765,697],{"class":696},[65,767,768],{"class":121},"actions\u002Fcheckout@v4\n",[65,770,771,773,775,777],{"class":67,"line":321},[65,772,760],{"class":696},[65,774,763],{"class":692},[65,776,697],{"class":696},[65,778,779],{"class":121},"actions\u002Fsetup-python@v5\n",[65,781,782,785],{"class":67,"line":327},[65,783,784],{"class":692}," with",[65,786,731],{"class":696},[65,788,789,792,794],{"class":67,"line":333},[65,790,791],{"class":692}," python-version",[65,793,697],{"class":696},[65,795,796],{"class":121},"\"3.11\"\n",[65,798,799,801,803,805],{"class":67,"line":339},[65,800,760],{"class":696},[65,802,693],{"class":692},[65,804,697],{"class":696},[65,806,807],{"class":121},"Cache Hypothesis Database\n",[65,809,810,813,815],{"class":67,"line":344},[65,811,812],{"class":692}," uses",[65,814,697],{"class":696},[65,816,817],{"class":121},"actions\u002Fcache@v3\n",[65,819,820,822],{"class":67,"line":350},[65,821,784],{"class":692},[65,823,731],{"class":696},[65,825,826,829,831],{"class":67,"line":356},[65,827,828],{"class":692}," path",[65,830,697],{"class":696},[65,832,833],{"class":121},".hypothesis\u002Fexamples\n",[65,835,836,839,841],{"class":67,"line":362},[65,837,838],{"class":692}," key",[65,840,697],{"class":696},[65,842,843],{"class":121},"hypothesis-db-${{ runner.os }}-${{ hashFiles('**\u002Frequirements.txt') }}\n",[65,845,846,849,851],{"class":67,"line":368},[65,847,848],{"class":692}," restore-keys",[65,850,697],{"class":696},[65,852,854],{"class":853},"szBVR","|\n",[65,856,858],{"class":67,"line":857},19,[65,859,860],{"class":121}," hypothesis-db-${{ runner.os }}-\n",[65,862,864],{"class":67,"line":863},20,[65,865,866],{"class":121}," - run: pip install -r requirements.txt pytest pytest-xdist hypothesis\n",[65,868,870],{"class":67,"line":869},21,[65,871,872],{"class":121}," - run: pytest -n auto --hypothesis-profile ci\n",[65,874,876],{"class":67,"line":875},22,[65,877,878],{"class":121}," env:\n",[65,880,882],{"class":67,"line":881},23,[65,883,884],{"class":121}," HYPOTHESIS_PROFILE: ci\n",[14,886,887,888,891,892,894,895,897,898,901],{},"The ",[18,889,890],{},"pytest -n auto"," flag enables ",[18,893,528],{}," parallelization. Hypothesis safely partitions test execution across workers, but each worker must maintain an isolated database to prevent SQLite locking. The ",[18,896,517],{}," automatically handles this by appending worker IDs to database paths. Ensure ",[18,899,900],{},"HYPOTHESIS_PROFILE=ci"," is exported in your environment to override local settings, preventing execution divergence between developer machines and CI runners.",[14,903,904,905,908,909,912],{},"Address flaky test isolation by pinning deterministic seeds during debugging. When a test fails intermittently in CI, extract the seed from the failure output (",[18,906,907],{},"Falsifying example: ... seed=123456789",") and run ",[18,910,911],{},"pytest -n 0 --hypothesis-seed=123456789"," locally. This guarantees exact reproduction of the CI environment's generation sequence. Maintain reproducibility while cutting wall-clock time through worker-isolated databases and aggressive cache restoration. Never disable parallelization to \"fix\" flakiness; instead, isolate the non-deterministic dependency and mock it.",[33,914,916],{"id":915},"advanced-edge-cases-anti-patterns-to-avoid","Advanced Edge Cases & Anti-Patterns to Avoid",[14,918,919,920,923,924,927,928,931],{},"Several recurring anti-patterns silently degrade Hypothesis performance. First, overusing ",[18,921,922],{},"@st.one_of()"," with heavy strategies forces the engine to evaluate multiple complex generators sequentially, increasing allocation overhead. Prefer ",[18,925,926],{},"@st.sampled_from()"," for discrete choices or flatten strategies into a single ",[18,929,930],{},"@st.builds()"," call.",[14,933,934,935,938],{},"Second, ignoring ",[18,936,937],{},"@settings(deadline)"," warnings masks underlying algorithmic complexity. A deadline breach indicates that your test function performs O(n²) or worse operations on generated data. Profile the assertion logic before increasing the threshold.",[14,940,941,942,945,946,949],{},"Third, misconfiguring ",[18,943,944],{},"@st.from_type()"," for complex Pydantic or SQLAlchemy models triggers recursive type resolution, often generating oversized, deeply nested objects. Override with explicit field constraints: ",[18,947,948],{},"@st.builds(Model, field=st.integers(min_value=0, max_value=10), optional_field=st.just(None))",".",[14,951,952,953,956],{},"Fourth, running stateful tests without ",[18,954,955],{},"@Rule(precondition=...)"," leads to exponential state space traversal. State machines require explicit guards to prevent invalid transitions. Always define preconditions for rules that mutate system state.",[14,958,959],{},[960,961,962],"strong",{},"Rapid Diagnosis Checklist:",[964,965,966,974,983,993,999],"ol",{},[967,968,969,970,973],"li",{},"Run ",[18,971,972],{},"pytest --hypothesis-show-statistics"," to isolate phase latency.",[967,975,976,977,979,980,982],{},"Replace ",[18,978,559],{}," with ",[18,981,555],{}," for constrained inputs.",[967,984,985,986,988,989,992],{},"Add ",[18,987,194],{},"\u002F",[18,990,991],{},"max_depth"," to all recursive or collection strategies.",[967,994,995,996,998],{},"Verify ",[18,997,434],{}," cache restoration in CI.",[967,1000,1001,1002,1004],{},"Ensure ",[18,1003,528],{}," workers use isolated database paths.",[33,1006,1008],{"id":1007},"frequently-asked-questions","Frequently Asked Questions",[14,1010,1011,1014,1015,1017,1018,1020,1021,1024],{},[960,1012,1013],{},"Why does my Hypothesis test run significantly slower on CI than locally?","\nCI environments typically lack the ",[18,1016,673],{}," cache, forcing full regeneration and shrinking. Mount or cache the ",[18,1019,434],{}," directory in your pipeline, and use ",[18,1022,1023],{},"@settings(database=DirectoryBasedExampleDatabase('\u002Ftmp\u002F.hypothesis'))"," to standardize paths across runners.",[14,1026,1027,1030,1031,1033,1034,1036,1037,1039],{},[960,1028,1029],{},"Can I safely use pytest-xdist with Hypothesis?","\nYes, but avoid sharing the same example database across workers. Set ",[18,1032,900],{}," and configure each worker with a unique database path or use ",[18,1035,521],{}," for stateless parallel runs. The ",[18,1038,517],{}," handles isolation automatically.",[14,1041,1042,1045,1046,1049,1050,1052,1053,1055,1056,1058],{},[960,1043,1044],{},"How do I diagnose if slowness comes from strategy generation or my test logic?","\nUse ",[18,1047,1048],{},"pytest --profile-svg"," or wrap your test body with ",[18,1051,159],{},". Hypothesis exposes ",[18,1054,155],{}," for granular phase timing. If generation dominates, constrain strategies; if shrinking dominates, add early ",[18,1057,555],{}," guards.",[14,1060,1061,1064,1065,1067,1068,1070],{},[960,1062,1063],{},"Is it safe to disable shrinking to speed up tests?","\nOnly for exploratory fuzzing or smoke tests. Disabling ",[18,1066,654],{}," sacrifices minimal repro generation, making debugging failures significantly harder. Use ",[18,1069,658],{}," temporarily, but re-enable for production pipelines.",[14,1072,1073,1076,1077,1080,1081,1084],{},[960,1074,1075],{},"How does @st.from_type() impact execution time for complex models?","\nIt recursively resolves nested types, often generating oversized objects. Override with explicit ",[18,1078,1079],{},"@st.builds(Model, field=st.integers(min_value=0, max_value=10))"," and disable optional fields via ",[18,1082,1083],{},"@st.just(None)"," to reduce combinatorial overhead.",[1086,1087,1088],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html pre.shiki code .sScJk, html code.shiki .sScJk{--shiki-default:#6F42C1;--shiki-dark:#B392F0}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}html pre.shiki code .sZZnC, html code.shiki .sZZnC{--shiki-default:#032F62;--shiki-dark:#9ECBFF}html pre.shiki code .sJ8bj, html code.shiki .sJ8bj{--shiki-default:#6A737D;--shiki-dark:#6A737D}html pre.shiki code .s9eBZ, html code.shiki .s9eBZ{--shiki-default:#22863A;--shiki-dark:#85E89D}html pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html pre.shiki code .szBVR, html code.shiki .szBVR{--shiki-default:#D73A49;--shiki-dark:#F97583}",{"title":61,"searchDepth":74,"depth":74,"links":1090},[1091,1092,1093,1094,1095,1096,1097],{"id":35,"depth":74,"text":36},{"id":172,"depth":74,"text":173},{"id":420,"depth":74,"text":421},{"id":545,"depth":74,"text":546},{"id":662,"depth":74,"text":663},{"id":915,"depth":74,"text":916},{"id":1007,"depth":74,"text":1008},"Property-based testing fundamentally shifts the verification paradigm from static, handcrafted inputs to exhaustive, algorithmically generated data spaces. This paradigm shift introduces a well-known performance paradox: the pursuit of comprehensive edge-case coverage inherently increases execution overhead. When Hypothesis-driven suites regress into multi-hour CI runs, developers often resort to blunt instruments like max_examples=10 or deadline=None, which artificially accelerate execution while silently degrading failure detection capabilities. A diagnostic-first methodology is required to isolate latency sources without sacrificing the mathematical guarantees of property-based testing. Speed optimizations must never compromise failure reproducibility or shrinking fidelity. Within the broader Property-Based & Fuzz Testing Strategies ecosystem, performance tuning is not about disabling Hypothesis features, but about aligning strategy complexity, phase execution, and infrastructure caching with the actual computational boundaries of your system under test.","md",{},"\u002Fproperty-based-fuzz-testing-strategies\u002Fhypothesis-framework-fundamentals\u002Freducing-hypothesis-test-execution-time",{"title":5,"description":1098},"property-based-fuzz-testing-strategies\u002Fhypothesis-framework-fundamentals\u002Freducing-hypothesis-test-execution-time\u002Findex","r0ZSZeJay0eabsB3urqB-uzF4T2OfDkMGH53biZ7EkU",1778004578989]