Hypothesmith property test

docs/contributing/4.-acknowledgements.md

@@ -184,6 +184,7 @@ Code Contributors
 - @nicolelodeon
 - Łukasz Langa (@ambv)
 - Grzegorz Pstrucha (@Gricha)
+- Zac Hatfield-Dodds (@Zac-HD)
 
 
 Documenters

tests/test_hypothesmith.py

@@ -0,0 +1,94 @@
+import ast
+import dis
+from typing import get_type_hints
+
+import hypothesis
+import libcst
+from hypothesis import strategies as st
+from hypothesmith import from_grammar, from_node
+
+import isort
+
+
+def _as_config(kw) -> isort.Config:
+    if "wrap_length" in kw and "line_length" in kw:
+        kw["wrap_length"], kw["line_length"] = sorted([kw["wrap_length"], kw["line_length"]])
+    try:
+        return isort.Config(**kw)
+    except ValueError:
+        kw["wrap_length"] = 0
+        return isort.Config(**kw)
+
+
+def _record_targets(code: str, prefix: str = "") -> str:
+    # target larger inputs - the Hypothesis engine will do a multi-objective
+    # hill-climbing search using these scores to generate 'better' examples.
+    nodes = list(ast.walk(ast.parse(code)))
+    import_nodes = [n for n in nodes if isinstance(n, (ast.Import, ast.ImportFrom))]
+    uniq_nodes = {type(n) for n in nodes}
+    instructions = list(dis.Bytecode(compile(code, "<string>", "exec")))
+    for value, label in [
+        (len(instructions), "instructions in bytecode"),
+        (len(import_nodes), "total number of import nodes"),
+        (len(uniq_nodes), "number of unique ast node types"),
+    ]:
+        hypothesis.target(float(value), label=prefix + label)
+    return code
+
+
+def configs(**force_strategies: st.SearchStrategy) -> st.SearchStrategy[isort.Config]:
+    """Generate arbitrary Config objects."""
+    skip = {
+        "line_ending",
+        "sections",
+        "known_future_library",
+        "forced_separate",
+        "lines_after_imports",
+        "lines_between_sections",
+        "lines_between_types",
+        "sources",
+        "virtual_env",
+        "conda_env",
+        "directory",
+        "formatter",
+        "formatting_function",
+    }
+    inferred_kwargs = {
+        k: st.from_type(v)
+        for k, v in get_type_hints(isort.settings._Config).items()
+        if k not in skip
+    }
+    specific = {
+        "line_length": st.integers(0, 200),
+        "wrap_length": st.integers(0, 200),
+        "indent": st.integers(0, 20).map(lambda n: n * " "),
+        "default_section": st.sampled_from(sorted(isort.settings.KNOWN_SECTION_MAPPING)),
+        "force_grid_wrap": st.integers(0, 20),
+        "profile": st.sampled_from(sorted(isort.settings.profiles)),
+    }
+    kwargs = {**inferred_kwargs, **specific, **force_strategies}
+    return st.fixed_dictionaries({}, optional=kwargs).map(_as_config)
+
+
+st.register_type_strategy(isort.Config, configs())
+
+
+@hypothesis.given(
+    source_code=st.lists(
+        from_grammar(auto_target=False)
+        | from_node(auto_target=False)
+        | from_node(libcst.Import, auto_target=False)
+        | from_node(libcst.ImportFrom, auto_target=False),
+        min_size=1,
+        max_size=10,
+    ).map("\n".join),
+    config=st.builds(isort.Config),
+    disregard_skip=st.booleans(),
+)
+@hypothesis.seed(suppress_health_check=[hypothesis.HealthCheck.too_slow])
+def test_isort_is_idempotent(source_code: str, config: isort.Config, disregard_skip: bool) -> None:
+    # NOTE: if this test finds a bug, please notify @Zac-HD so that it can be added to the
+    #       Hypothesmith trophy case.  This really helps with research impact evaluations!
+    _record_targets(source_code)
+    result = isort.code(source_code, config=config, disregard_skip=disregard_skip)
+    assert result == isort.code(result, config=config, disregard_skip=disregard_skip)