Efficiently computes derivatives of NumPy code.
This report summarizes scanner evidence for HIPS/autograd, a Python project with 7,539 GitHub stars. SynthScan examined 15,506 lines of code across 120 source files, recording 206 pattern matches distributed across 12 syntactic categories. The legacy adjusted pattern signal is 15.9 (Elevated pattern signal); it is not an authorship estimate.
Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.
Use each vector to guide manual verification of the underlying findings.
Can maintainers explain intent, assumptions, and trade-offs? 129 finding(s).
Are imports declared and package/API names verified? 74 finding(s).
Are wrappers, guards, and repeated structures necessary? 111 finding(s).
Which changes need focused human review? 206 finding(s).
This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.
Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.
This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.
The scanner identified 206 distinct pattern matches across 12 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.
Reading the findings table: The Severity column indicates the configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | autograd/builtins.py | 121 | def grad_sequence_extend_right(argnum, ans, args, kwargs): | CODE |
| LOW | autograd/builtins.py | 134 | def grad_sequence_extend_left(argnum, ans, args, kwargs): | CODE |
| LOW | autograd/core.py | 378 | def deprecated_defvjp_is_zero(primitive_fun): | CODE |
| LOW | autograd/core.py | 407 | def primitive_with_deprecation_warnings(f_raw): | CODE |
| LOW | autograd/numpy/numpy_wrapper.py | 101 | def _array_from_scalar_or_array(array_args, array_kwargs, scalar): | CODE |
| LOW | autograd/numpy/numpy_vjps.py | 923 | def array_from_args_gradmaker(argnum, ans, args, kwargs): | CODE |
| LOW | autograd/numpy/numpy_vjps.py | 930 | def array_from_scalar_or_array_gradmaker(ans, array_args, array_kwargs, scarray): | CODE |
| LOW | autograd/numpy/numpy_jvps.py | 244 | def fwd_grad_concatenate_args(argnum, g, ans, axis_args, kwargs): | CODE |
| LOW | autograd/scipy/stats/multivariate_normal.py | 19 | def generalized_outer_product(x): | CODE |
| LOW | tests/test_linalg.py | 17 | def check_symmetric_matrix_grads(fun, **grad_check_kwargs): | CODE |
| LOW | tests/test_linalg.py | 204 | def test_frobenius_norm_complex(): | CODE |
| LOW | tests/test_linalg.py | 222 | def test_frobenius_norm_axis_complex(): | CODE |
| LOW | tests/test_linalg.py | 243 | def test_vector_norm_ord_complex(size, ord): | CODE |
| LOW | tests/test_linalg.py | 281 | def test_norm_nuclear_complex(): | CODE |
| LOW | tests/test_linalg.py | 300 | def test_norm_nuclear_axis_complex(): | CODE |
| LOW | tests/test_linalg.py | 332 | def test_eigvalh_lower_broadcasting(): | CODE |
| LOW | tests/test_linalg.py | 343 | def test_eigvalh_upper_broadcasting(): | CODE |
| LOW | tests/test_linalg.py | 357 | def test_eigvalh_lower_complex(): | CODE |
| LOW | tests/test_linalg.py | 367 | def test_eigvalh_upper_complex(): | CODE |
| LOW | tests/test_linalg.py | 420 | def test_cholesky_reparameterization_trick(): | CODE |
| LOW | tests/test_misc.py | 96 | def test_flatten_nodes_in_containers(): | CODE |
| LOW⚡ | tests/test_scipy.py | 102 | def test_norm_logpdf_broadcast(): | CODE |
| LOW⚡ | tests/test_scipy.py | 105 | def test_norm_logcdf_broadcast(): | CODE |
| LOW⚡ | tests/test_scipy.py | 108 | def test_norm_logsf_broadcast(): | CODE |
| LOW⚡ | tests/test_scipy.py | 120 | def test_poisson_cdf_broadcast(): | CODE |
| LOW⚡ | tests/test_scipy.py | 123 | def test_poisson_logpmf_broadcast(): | CODE |
| LOW⚡ | tests/test_scipy.py | 126 | def test_poisson_pmf_broadcast(): | CODE |
| LOW | tests/test_scipy.py | 198 | def test_mvn_logpdf_broadcast(): | CODE |
| LOW | tests/test_scipy.py | 211 | def normalized_dirichlet_logpdf(x, alpha): | CODE |
| LOW | tests/test_scipy.py | 223 | def test_dirichlet_logpdf_alpha(): | CODE |
| LOW | tests/test_scipy.py | 267 | def test_convolve_generalization(): | CODE |
| LOW⚡ | tests/test_vspaces.py | 49 | def compatibility_of_scalar_mul_with_field_mul(x, a, b): | CODE |
| LOW⚡ | tests/test_vspaces.py | 52 | def identity_element_of_scalar_mul(x): | CODE |
| LOW⚡ | tests/test_vspaces.py | 55 | def distributivity_of_scalar_mul_wrt_vector_add(x, y, a): | CODE |
| LOW⚡ | tests/test_vspaces.py | 58 | def distributivity_of_scalar_mul_wrt_scalar_add(x, a, b): | CODE |
| LOW⚡ | tests/test_vspaces.py | 65 | def scalar_mul_preserves_vspace(x, a): | CODE |
| LOW | tests/test_vspaces.py | 142 | def test_array_vspace_complex(): | CODE |
| LOW | tests/test_graphs.py | 83 | def test_mutating_outgrad_from_indexing(): | CODE |
| LOW | tests/test_graphs.py | 95 | def test_complex_mutating_outgrad_from_indexing(): | CODE |
| LOW | tests/test_graphs.py | 109 | def test_complex_separate_real_and_imaginary(): | CODE |
| LOW | tests/test_graphs.py | 132 | def test_third_derivative_other_args(): | CODE |
| LOW | tests/test_graphs.py | 154 | def test_singleton_array_output(): | CODE |
| LOW | tests/test_graphs.py | 184 | def test_assignment_raises_error(): | CODE |
| LOW | tests/numpy_utils.py | 60 | def binary_ufunc_check_no_same_args(fun, lims_A=None, lims_B=None, test_complex=True, **kwargs): | CODE |
| LOW⚡ | tests/test_jacobian.py | 9 | def test_jacobian_against_grad(): | CODE |
| LOW⚡ | tests/test_jacobian.py | 15 | def test_jacobian_scalar_to_vector(): | CODE |
| LOW⚡ | tests/test_jacobian.py | 21 | def test_jacobian_against_stacked_grads(): | CODE |
| LOW | tests/test_jacobian.py | 37 | def test_jacobian_higher_order(): | CODE |
| LOW | tests/test_fft.py | 270 | def test_rfftn_odd_not_implemented(): | CODE |
| LOW | tests/test_ufunc_dispatch.py | 22 | def test_grad_through_dataarray_binary_ufunc(): | CODE |
| LOW | tests/test_ufunc_dispatch.py | 35 | def test_grad_through_dataarray_reversed_operand(): | CODE |
| LOW | tests/test_ufunc_dispatch.py | 48 | def test_grad_through_dataarray_unary_ufunc(): | CODE |
| LOW | tests/test_ufunc_dispatch.py | 62 | def test_grad_through_dataarray_transcendental_ufunc(): | CODE |
| LOW | tests/test_ufunc_dispatch.py | 76 | def test_grad_through_two_dataarrays_holding_boxes(): | CODE |
| LOW | tests/test_ufunc_dispatch.py | 92 | def test_grad_through_dataarray_box_plus_plain_dataarray(): | CODE |
| LOW | tests/test_ufunc_dispatch.py | 106 | def test_grad_through_dataarray_box_times_plain_ndarray(): | CODE |
| LOW | tests/test_ufunc_dispatch.py | 120 | def test_grad_through_dataarray_box_times_python_scalar(): | CODE |
| LOW | tests/test_ufunc_dispatch.py | 134 | def test_grad_through_dataarray_division(): | CODE |
| LOW | tests/test_ufunc_dispatch.py | 147 | def test_grad_through_dataarray_exp(): | CODE |
| LOW | tests/test_ufunc_dispatch.py | 161 | def test_grad_through_dataarray_maximum(): | CODE |
| 48 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | autograd/extend.py | 2 | CODE | |
| LOW | autograd/extend.py | 2 | CODE | |
| LOW | autograd/extend.py | 2 | CODE | |
| LOW | autograd/extend.py | 2 | CODE | |
| LOW | autograd/extend.py | 2 | CODE | |
| LOW | autograd/extend.py | 2 | CODE | |
| LOW | autograd/extend.py | 2 | CODE | |
| LOW | autograd/extend.py | 2 | CODE | |
| LOW | autograd/extend.py | 2 | CODE | |
| LOW | autograd/extend.py | 2 | CODE | |
| LOW | autograd/extend.py | 2 | CODE | |
| LOW | autograd/extend.py | 2 | CODE | |
| LOW | autograd/extend.py | 16 | CODE | |
| LOW | autograd/extend.py | 16 | CODE | |
| LOW | autograd/extend.py | 16 | CODE | |
| LOW | autograd/extend.py | 16 | CODE | |
| LOW | autograd/__init__.py | 1 | CODE | |
| LOW | autograd/__init__.py | 3 | CODE | |
| LOW | autograd/__init__.py | 3 | CODE | |
| LOW | autograd/__init__.py | 3 | CODE | |
| LOW | autograd/__init__.py | 3 | CODE | |
| LOW | autograd/__init__.py | 3 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/__init__.py | 4 | CODE | |
| LOW | autograd/tracer.py | 10 | CODE | |
| LOW | autograd/misc/__init__.py | 1 | CODE | |
| LOW | autograd/misc/__init__.py | 2 | CODE | |
| LOW | autograd/numpy/__init__.py | 6 | CODE | |
| LOW | autograd/numpy/__init__.py | 6 | CODE | |
| LOW | autograd/numpy/__init__.py | 6 | CODE | |
| LOW | autograd/numpy/__init__.py | 6 | CODE | |
| LOW | autograd/numpy/__init__.py | 6 | CODE | |
| LOW | autograd/numpy/__init__.py | 6 | CODE | |
| LOW | autograd/numpy/__init__.py | 6 | CODE | |
| LOW | autograd/numpy/__init__.py | 7 | CODE | |
| LOW | autograd/numpy/__init__.py | 8 | CODE | |
| LOW | autograd/scipy/__init__.py | 1 | CODE | |
| LOW | autograd/scipy/__init__.py | 1 | CODE | |
| LOW | autograd/scipy/__init__.py | 1 | CODE | |
| LOW | autograd/scipy/__init__.py | 1 | CODE | |
| LOW | autograd/scipy/stats/__init__.py | 1 | CODE | |
| LOW | autograd/scipy/stats/__init__.py | 1 | CODE | |
| LOW | autograd/scipy/stats/__init__.py | 1 | CODE | |
| 5 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | autograd/scipy/signal.py | 15 | f"Mode {mode} undefined, it can be one of 'valid', 'full', and 'same'" | CODE |
| HIGH | autograd/scipy/signal.py | 157 | f"Mode {mode} undefined, it can be one of 'valid', 'full', and 'same'" | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | autograd/scipy/stats/__init__.py | 7 | except AttributeError: | CODE |
| HIGH | autograd/scipy/stats/__init__.py | 12 | except AttributeError: | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | tests/test_truediv.py | 1 | # This file is to check that future division works. | COMMENT |
| MEDIUM | examples/gaussian_process.py | 41 | # Define an example covariance function. | COMMENT |
| MEDIUM | examples/natural_gradient_black_box_svi.py | 28 | # Define the natural gradient | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | examples/variational_autoencoder.py | 155 | fake_data = generate_from_prior(gen_params, 20, latent_dim, seed) | CODE |
| LOW | examples/variational_autoencoder.py | 156 | save_images(fake_data, "vae_samples.png", vmin=0, vmax=1) | CODE |
| LOW | examples/generative_adversarial_net.py | 68 | fake_data = generate_from_noise(gen_params, num_samples, noise_dim, rs) | CODE |
| LOW | examples/generative_adversarial_net.py | 69 | logprobs_fake = logsigmoid(neural_net_predict(dsc_params, fake_data)) | CODE |
| LOW⚡ | examples/generative_adversarial_net.py | 165 | fake_data = generate_from_noise(gen_params, 20, noise_dim, seed) | CODE |
| LOW⚡ | examples/generative_adversarial_net.py | 167 | probs_fake = np.mean(sigmoid(neural_net_predict(dsc_params, fake_data))) | CODE |
| LOW⚡ | examples/generative_adversarial_net.py | 170 | save_images(fake_data, "gan_samples.png", vmin=0, vmax=1) | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | examples/gplvm.py | 6 | # In this example, we optimize X and the hyperparameters of the GP, but | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | autograd/tracer.py | 61 | # (It's possible our handling attempt below will get the first shot; the handlers | COMMENT |
| LOW | tests/test_graphs.py | 201 | COMMENT | |
| LOW | tests/test_performance.py | 1 | # TODO: | COMMENT |
| LOW | examples/define_gradient.py | 21 | COMMENT | |
| LOW | examples/natural_gradient_black_box_svi.py | 21 | mu_density = np.sum(norm.logpdf(Y, mu, np.exp(log_sigma)), axis=1) | COMMENT |
| LOW | examples/gplvm.py | 1 | # Implements a Gaussian process latent-variable model. | COMMENT |
| LOW | .github/workflows/test.yml | 41 | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | autograd/tracer.py | 119 | CODE | |
| LOW | autograd/numpy/numpy_wrapper.py | 25 | CODE | |
| LOW | autograd/numpy/linalg.py | 284 | CODE | |
| LOW | autograd/numpy/linalg.py | 285 | CODE | |
| LOW | autograd/numpy/numpy_jvps.py | 209 | CODE | |
| LOW | autograd/scipy/special.py | 137 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | autograd/numpy/linalg.py | 263 | # dimensions, so we just call a generic LU solve instead of directly using | COMMENT |
| LOW | autograd/numpy/numpy_boxes.py | 20 | # Basic array attributes just pass through | COMMENT |
| MEDIUM | examples/natural_gradient_black_box_svi.py | 46 | # is small (which leads to more robust/less chaotic ascent). | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | autograd/tracer.py | 89 | except Exception as e: | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | autograd/numpy/numpy_vjps.py | 287 | def helper(g, n): | CODE |