Fuzz introspector
For issues and ideas: https://github.com/ossf/fuzz-introspector/issues
Project coverage

Table of contents

Project overview: opt_einsum
High level conclusions
Reachability and coverage overview
Fuzzers overview
Project functions overview
Fuzzer details
Fuzzer: fuzz_parser
Call tree
Fuzz blockers
Runtime coverage analysis
Files reached
Analyses and suggestions
Optimal target analysis
Remaining optimal interesting functions
All functions overview
Runtime coverage analysis
Complex functions with low coverage
Files and Directories in report
Files in report
Directories in report
Metadata section
Function call coverage
Function in each files in report
Report generation date: 2023-03-16

Project overview: opt_einsum

High level conclusions

Reachability and coverage overview

Functions statically reachable by fuzzers
0.48%
35/7238
Cyclomatic complexity statically reachable by fuzzers
0.47%
114/24272
Runtime code coverage of functions
28.0%
2062 / 7238

Fuzzers overview

Fuzzer Fuzzer filename Functions Reached Functions unreached Fuzzer depth Files reached Basic blocks reached Cyclomatic complexity Details
fuzz_parser /src/fuzz_parser.py 36 7510 4 3 26 114 fuzz_parser.py

Project functions overview

The following table shows data about each function in the project. The functions included in this table correspond to all functions that exist in the executables of the fuzzers. As such, there may be functions that are from third-party libraries.

For further technical details on the meaning of columns in the below table, please see the Glossary .

Func name Functions filename Args Function call depth Reached by Fuzzers Fuzzers runtime hit Func lines hit % I Count BB Count Cyclomatic complexity Functions reached Reached by functions Accumulated cyclomatic complexity Undiscovered complexity

Fuzzer details

Fuzzer: fuzz_parser

Call tree

The calltree shows the control flow of the fuzzer. This is overlaid with coverage information to display how much of the potential code a fuzzer can reach is in fact covered at runtime. In the following there is a link to a detailed calltree visualisation as well as a bitmap showing a high-level view of the calltree. For further information about these topics please see the glossary for full calltree and calltree overview

Full calltree

Call tree overview bitmap:

The distribution of callsites in terms of coloring is
Color Runtime hitcount Callsite count Percentage
red 0 31 46.9%
gold [1:9] 0 0.0%
yellow [10:29] 0 0.0%
greenyellow [30:49] 0 0.0%
lawngreen 50+ 35 53.0%
All colors 66 100

Fuzz blockers

The following nodes represent call sites where fuzz blockers occur.

Amount of callsites blocked Calltree index Parent function Callsite Largest blocked function
20 11 opt_einsum.parser.possibly_convert_to_numpy call site: 00011 opt_einsum.parser.convert_interleaved_input
3 1 ...fuzz_parser.TestOneInput call site: 00001 fdp.ConsumeFloat
3 45 opt_einsum.parser.parse_einsum_input call site: 00045 .len
2 7 opt_einsum.parser.parse_einsum_input call site: 00007 .hasattr
1 38 opt_einsum.parser.parse_einsum_input call site: 00038 opt_einsum.parser.get_symbol
1 49 opt_einsum.parser.parse_einsum_input call site: 00049 sub.replace
1 64 opt_einsum.parser.parse_einsum_input call site: 00064 fdp.ConsumeBool

Runtime coverage analysis

Covered functions
429
Functions that are reachable but not covered
32
Reachable functions
36
Percentage of reachable functions covered
11.11%
NB: The sum of covered functions and functions that are reachable but not covered need not be equal to Reachable functions . This is because the reachability analysis is an approximation and thus at runtime some functions may be covered that are not included in the reachability analysis. This is a limitation of our static analysis capabilities.
Warning: The number of covered functions are larger than the number of reachable functions. This means that there are more functions covered at runtime than are extracted using static analysis. This is likely a result of the static analysis component failing to extract the right call graph or the coverage runtime being compiled with sanitizers in code that the static analysis has not analysed. This can happen if lto/gold is not used in all places that coverage instrumentation is used.
Function name source code lines source lines hit percentage hit

Files reached

filename functions hit
/ 1
...fuzz_parser 4
opt_einsum.parser 31

Analyses and suggestions

Optimal target analysis

Remaining optimal interesting functions

The following table shows a list of functions that are optimal targets. Optimal targets are identified by finding the functions that in combination, yield a high code coverage.

Func name Functions filename Arg count Args Function depth hitcount instr count bb count cyclomatic complexity Reachable functions Incoming references total cyclomatic complexity Unreached complexity
numpy.f2py.f2py2e.run_main numpy.f2py.f2py2e 1 ['N/A'] 6 0 9 8 6 235 2 900 882
numpy.f2py.crackfortran.postcrack numpy.f2py.crackfortran 3 ['N/A', 'N/A', 'N/A'] 6 0 9 20 11 189 4 714 578
numpy.lib.shape_base.apply_along_axis numpy.lib.shape_base 5 ['N/A', 'N/A', 'N/A', 'N/A', 'N/A'] 5 0 1 2 4 177 0 598 562
numpy.linalg.setup.configuration numpy.linalg.setup 2 ['N/A', 'N/A'] 5 0 4 2 4 179 0 597 516
numpy.distutils.command.build_src.build_src.build_sources numpy.distutils.command.build_src 1 ['N/A'] 3 0 6 2 4 171 1 569 440
numpy.f2py.crackfortran.readfortrancode numpy.f2py.crackfortran 3 ['N/A', 'N/A', 'N/A'] 5 0 14 42 20 178 3 701 336
numpy.distutils.command.build_clib.build_clib.run numpy.distutils.command.build_clib 1 ['N/A'] 5 0 15 8 6 135 0 435 329

Implementing fuzzers that target the above functions will improve reachability such that it becomes:

Functions statically reachable by fuzzers
13.6%
987/7238
Cyclomatic complexity statically reachable by fuzzers
14.8%
3593 / 24272

All functions overview

If you implement fuzzers for these functions, the status of all functions in the project will be:

Func name Functions filename Args Function call depth Reached by Fuzzers Fuzzers runtime hit Func lines hit % I Count BB Count Cyclomatic complexity Functions reached Reached by functions Accumulated cyclomatic complexity Undiscovered complexity

Runtime coverage analysis

This section shows analysis of runtime coverage data.

For futher technical details on how this section is generated, please see the Glossary .

Complex functions with low coverage

Func name Function total lines Lines covered at runtime percentage covered Reached by fuzzers
opt_einsum.paths.BranchBound.__call__._branch_iterate._assess_candidate 50 0 0.0% []
opt_einsum.paths.ssa_greedy_optimize 64 0 0.0% []
opt_einsum.paths.DynamicProgramming.__call__ 52 0 0.0% []
opt_einsum.blas.can_blas 34 0 0.0% []
opt_einsum.blas.tensor_blas 39 0 0.0% []
numpy._pytesttester.PytestTester.__call__ 39 0 0.0% []
numpy.core.numeric.tensordot 54 0 0.0% []
numpy.core.numeric.cross 61 1 1.639% []
numpy.core.function_base.linspace 37 0 0.0% []
numpy.core.function_base.geomspace 33 0 0.0% []
numpy.lib.polynomial.polyfit 48 0 0.0% []
numpy.lib.polynomial.poly1d.__str__.fmt_float 35 0 0.0% []
numpy.lib.utils.who 41 0 0.0% []
numpy.lib.utils.info 69 0 0.0% []
numpy.lib.utils._lookfor_generate_cache 89 0 0.0% []
numpy.core.einsumfunc._greedy_path 34 0 0.0% []
numpy.core.einsumfunc._can_dot 31 0 0.0% []
numpy.core.einsumfunc._parse_einsum_input 103 0 0.0% []
numpy.core.einsumfunc.einsum_path 107 0 0.0% []
numpy.core.einsumfunc.einsum 42 0 0.0% []
numpy.matrixlib.defmatrix.matrix.__new__ 35 0 0.0% []
numpy.lib.npyio._read 98 1 1.020% []
numpy.lib.npyio.savetxt.WriteWrap.first_write 67 0 0.0% []
numpy.lib.npyio.genfromtxt 169 0 0.0% []
numpy.lib.npyio.genfromtxt.tobytes_first 77 0 0.0% []
numpy.lib.npyio.genfromtxt.encode_unicode_cols 79 1 1.265% []
numpy.ma.core.MaskedArray.__new__ 51 16 31.37% []
numpy.ma.core.MaskedArray.__getitem__._scalar_heuristic 41 0 0.0% []
numpy.ma.core.MaskedArray.__setitem__ 50 3 6.0% []
numpy.ma.core.MaskedArray.__setmask__ 37 3 8.108% []
numpy.ma.core.MaskedArray.__repr__ 33 0 0.0% []
numpy.ma.core.MaskedArray.var 35 1 2.857% []
numpy.core._internal._array_descr 34 0 0.0% []
numpy.core._internal._commastring 33 0 0.0% []
numpy.core._internal.__dtype_from_pep3118 78 0 0.0% []
numpy.lib.arraypad._set_reflect_both 35 0 0.0% []
numpy.lib.arraypad.pad 74 0 0.0% []
numpy.core.memmap.memmap.__new__ 52 0 0.0% []
numpy.ctypeslib.ndpointer 45 0 0.0% []
numpy.polynomial.chebyshev._zseries_div 31 0 0.0% []
numpy.polynomial.chebyshev.chebint 37 0 0.0% []
numpy.core._machar.MachAr._do_init 178 0 0.0% []
numpy.linalg.linalg.matrix_power 32 0 0.0% []
numpy.linalg.linalg.qr 47 0 0.0% []
numpy.linalg.linalg.svd 41 0 0.0% []
numpy.linalg.linalg.lstsq 38 0 0.0% []
numpy.linalg.linalg.norm 82 0 0.0% []
numpy.core._methods._var 33 0 0.0% []
numpy.core.arrayprint._formatArray.recurser 58 0 0.0% []
numpy.core.arrayprint.FloatingFormat.fillFormat 56 0 0.0% []
numpy.lib.index_tricks.nd_grid.__getitem__ 55 2 3.636% []
numpy.lib.index_tricks.AxisConcatenator.__getitem__ 82 0 0.0% []
numpy.lib.function_base.select 31 0 0.0% []
numpy.lib.function_base.gradient 113 0 0.0% []
numpy.lib.function_base.diff 37 0 0.0% []
numpy.lib.function_base.cov 69 0 0.0% []
numpy.lib.function_base._ureduce 33 0 0.0% []
numpy.lib.function_base._median 31 0 0.0% []
numpy.lib.function_base._quantile 34 0 0.0% []
numpy.lib.function_base.delete 89 0 0.0% []
numpy.lib.function_base.insert 67 0 0.0% []
numpy.ma.extras.apply_along_axis 69 1 1.449% []
numpy.ma.extras.average 31 0 0.0% []
numpy.ma.extras._median 36 0 0.0% []
numpy.ma.extras.corrcoef 31 3 9.677% []
numpy.lib.nanfunctions.nanvar 34 0 0.0% []
numpy.core.defchararray.array 40 0 0.0% []
numpy.lib.histograms._get_bin_edges 40 0 0.0% []
numpy.lib.histograms.histogram 54 0 0.0% []
numpy.lib.histograms.histogramdd 57 0 0.0% []
numpy.lib.format._read_array_header 37 0 0.0% []
numpy.lib.format.read_array 32 0 0.0% []
numpy.lib._iotools.NameValidator.validate 38 0 0.0% []
numpy.lib._iotools.StringConverter.__init__ 46 0 0.0% []
numpy.core.records.fromfile 33 0 0.0% []
numpy.core.records.array 41 0 0.0% []
numpy.lib.arraysetops.in1d 69 0 0.0% []
numpy.polynomial.hermite_e.hermeint 34 0 0.0% []
numpy.polynomial.polyutils._fit 53 0 0.0% []
numpy.polynomial.polynomial.polyint 35 0 0.0% []
numpy.polynomial.hermite.hermint 34 0 0.0% []
numpy.polynomial.legendre.legint 38 0 0.0% []
numpy.polynomial._polybase.ABCPolyBase._repr_latex_ 33 0 0.0% []
numpy.polynomial.laguerre.lagint 35 0 0.0% []
opt_einsum.contract.contract_path 77 0 0.0% []
opt_einsum.contract._core_contract 36 0 0.0% []
opt_einsum.backends.torch.tensordot 31 0 0.0% []

Files and Directories in report

This section shows which files and directories are considered in this report. The main reason for showing this is fuzz introspector may include more code in the reasoning than is desired. This section helps identify if too many files/directories are included, e.g. third party code, which may be irrelevant for the threat model. In the event too much is included, fuzz introspector supports a configuration file that can exclude data from the report. See the following link for more information on how to create a config file: link

Files in report

Source file Reached by Covered by
[] []
numpy.distutils.command.build_scripts [] []
numpy.lib._datasource [] []
numpy.distutils.from_template [] []
numpy.core.tests.examples.limited_api.setup [] []
unittest [] []
numpy.core.umath [] []
numpy.distutils.fcompiler.compaq [] []
numpy.distutils.command.install [] []
opt_einsum.helpers [] []
numpy.lib.histograms [] []
weakref [] []
numpy.core._methods [] []
itertools [] []
numpy.typing.tests.data.pass.simple_py3 [] []
setuptools [] []
numpy.typing.tests.data.pass.comparisons [] []
numpy.lib.npyio [] []
opt_einsum.backends.jax [] []
numpy.distutils.system_info [] []
numpy.array_api._dtypes [] []
numpy.setup [] []
numpy.distutils.fcompiler.fujitsu [] []
hashlib [] []
numpy.typing.tests.data.pass.array_constructors [] []
platform [] []
typing [] []
numpy.core.einsumfunc [] []
numpy.core.shape_base [] []
builtins [] []
charset_normalizer [] []
numpy.core._dtype_ctypes [] []
numpy._typing._nbit [] []
warnings [] []
numpy.polynomial.hermite [] []
pipes [] []
numpy.matrixlib.setup [] []
torch [] []
numpy.core.getlimits [] []
numpy.typing.tests.data.pass.scalars [] []
opt_einsum._version [] []
numpy.fft._pocketfft [] []
numpy.lib.function_base [] []
opt_einsum.backends.theano [] []
curses [] []
numpy._pyinstaller.test_pyinstaller [] []
numpy.typing.tests.data.pass.flatiter [] []
numpy.typing.tests.data.pass.numeric [] []
numpy.typing.tests.data.pass.ufuncs [] []
numpy.core.numerictypes [] []
numpy.f2py.func2subr [] []
distutils [] []
numpy.array_api._statistical_functions [] []
numpy.distutils [] []
numpy.distutils.command.autodist [] []
numpy.core._string_helpers [] []
numpy.distutils.msvc9compiler [] []
pydoc [] []
numpy.array_api.linalg [] []
traceback [] []
opt_einsum.blas [] []
numpy.distutils.command.build_py [] []
numpy.core._dtype [] []
numpy.distutils.fcompiler.mips [] []
numpy.array_api._searching_functions [] []
numpy.distutils.fcompiler [] []
numpy.lib.polynomial [] []
numpy.lib.recfunctions [] []
numpy.core._machar [] []
textwrap [] []
shutil [] []
numpy.doc.constants [] []
decimal [] []
json [] []
numpy.typing.tests.data.pass.ndarray_shape_manipulation [] []
numpy.distutils.fcompiler.vast [] []
numpy._typing._nested_sequence [] []
numpy.distutils.misc_util [] []
numpy.distutils.fcompiler.nv [] []
numpy.distutils.fcompiler.nag [] []
numpy.testing [] []
numpy._version [] []
numpy.distutils.cpuinfo [] []
numpy.f2py.cfuncs [] []
numpy.random._examples.numba.extending [] []
ctypes [] []
numpy.typing.tests.data.pass.array_like [] []
numpy.core.tests.examples.cython.setup [] []
numpy.typing.tests.data.pass.fromnumeric [] []
numpy.polynomial.polynomial [] []
Cython [] []
opt_einsum.backends.torch [] []
numpy.distutils.command.config [] []
numpy.f2py.f90mod_rules [] []
numpy.ma.core [] []
numpy.lib.index_tricks [] []
numpy.conftest [] []
numpy.distutils.fcompiler.g95 [] []
numpy.core._internal [] []
operator [] []
numpy.distutils.extension [] []
cupy [] []
numpy.random._pickle [] []
numpy.polynomial [] []
collections [] []
numpy.typing.tests.data.pass.arrayterator [] []
numpy.lib.utils [] []
_mt19937 [] []
numpy.distutils.__config__ [] []
struct [] []
abc [] []
pickle5 [] []
_thread [] []
numpy.distutils.fcompiler.pathf95 [] []
numpy.f2py.rules [] []
numpy.compat._pep440 [] []
numpy.distutils.pathccompiler [] []
numpy.typing.mypy_plugin [] []
numpy.distutils.ccompiler [] []
opt_einsum.backends.object_arrays [] []
_generator [] []
numpy.ma.mrecords [] []
numpy.lib.scimath [] []
numpy._pyinstaller [] []
numpy.fft.helper [] []
difflib [] []
numpy.distutils.mingw32ccompiler [] []
numpy.lib.shape_base [] []
numpy.typing.tests.data.pass.dtype [] []
pprint [] []
numpy.typing.tests.data.pass.lib_utils [] []
tempfile [] []
psutil [] []
atheris [] []
numpy.core.cversions [] []
numpy.typing.tests.data.pass.arithmetic [] []
numpy.array_api._array_object [] []
_multiarray_umath [] []
numpy.lib.mixins [] []
numpy._pyinstaller.hook-numpy [] []
numpy.distutils.setup [] []
contextvars [] []
numpy.distutils.log [] []
numpy.array_api.setup [] []
numpy.core [] []
re [] []
numpy._typing._generic_alias [] []
numpy.typing.tests.data.pass.literal [] []
numpy.typing.tests.data.pass.ndarray_conversion [] []
numpy.core._add_newdocs [] []
sysconfig [] []
pathlib [] []
genapi [] []
numpy.testing.setup [] []
numpy.lib.user_array [] []
numpy.f2py.__version__ [] []
numpy.array_api._sorting_functions [] []
numpy.distutils.command.egg_info [] []
numpy.distutils.armccompiler [] []
numpy._typing._shape [] []
numpy._typing [] []
numpy.typing.tests.data.pass.numerictypes [] []
numpy.f2py.cb_rules [] []
os [] []
numpy.lib.nanfunctions [] []
numpy.distutils.numpy_distribution [] []
atexit [] []
numpy.core.generate_numpy_api [] []
numpy.distutils.fcompiler.hpux [] []
random [] []
numpy.array_api [] []
zipfile [] []
opt_einsum.backends.cupy [] []
doctest [] []
numpy_distutils [] []
gc [] []
nose [] []
numpy.testing.print_coercion_tables [] []
numpy.core._ufunc_config [] []
numpy.typing [] []
numpy.typing.tests.data.pass.simple [] []
fileinput [] []
numpy.typing.tests.data.pass.arrayprint [] []
_dummy_thread [] []
numpy [] []
numpy.lib.stride_tricks [] []
numpy.core.setup [] []
numpy.ma.timer_comparison [] []
numpy.distutils.fcompiler.sun [] []
numpy.distutils.command.build_src [] []
numpy.core._exceptions [] []
opt_einsum.path_random [] []
numpy.core.numeric [] []
code_generators [] []
numpy.lib.type_check [] []
concurrent [] []
numpy.distutils.ccompiler_opt [] []
numpy.core._add_newdocs_scalars [] []
numpy.core.arrayprint [] []
numpy.__config__ [] []
win32pdh [] []
numpy.polynomial.hermite_e [] []
numpy.distutils.unixccompiler [] []
numpy.random._examples.numba.extending_distributions [] []
mmap [] []
numpy.typing.tests.data.pass.index_tricks [] []
numpy.f2py.setup [] []
numpy.matlib [] []
heapq [] []
numpy._typing.setup [] []
numpy.lib._iotools [] []
numpy.compat.setup [] []
numpy.typing.tests.data.pass.random [] []
numpy.distutils.fcompiler.arm [] []
numpy.core._type_aliases [] []
numpy.core.setup_common [] []
types [] []
numpy.typing.tests.data.pass.modules [] []
numpy.array_api._utility_functions [] []
numpy.testing._private [] []
numpy.distutils.command [] []
numpy.linalg.linalg [] []
numpy.compat.py3k [] []
numpy.array_api._typing [] []
numpy.distutils.command.install_data [] []
_pocketfft_internal [] []
_pcg64 [] []
numpy.polynomial.chebyshev [] []
mypy [] []
numpy.distutils.fcompiler.gnu [] []
numpy.distutils.fcompiler.lahey [] []
scipy [] []
numpy.distutils.conv_template [] []
numpy.lib.setup [] []
glob [] []
copyreg [] []
numpy.random._examples.cython.setup [] []
numpy.distutils.command.bdist_rpm [] []
numpy.testing._private.utils [] []
numpy.ma.setup [] []
functools [] []
numpy._typing._extended_precision [] []
opt_einsum.contract [] []
numpy.polynomial.polyutils [] []
opt_einsum [] []
numpy.ma.testutils [] []
numpy.core.function_base [] []
numpy.distutils.exec_command [] []
...fuzz_parser ['fuzz_parser'] []
numpy.distutils.command.config_compiler [] []
numpy.core.overrides [] []
numpy.distutils.fcompiler.pg [] []
numpy._pyinstaller.pyinstaller-smoke [] []
opt_einsum.parser ['fuzz_parser'] []
numpy.core.fromnumeric [] []
numpy.core.multiarray [] []
numpy.typing.tests.data.pass.bitwise_ops [] []
numpy.array_api._data_type_functions [] []
numpy.lib.format [] []
numpy.linalg [] []
multiprocessing [] []
opt_einsum.backends.tensorflow [] []
numpy.array_api._set_functions [] []
opt_einsum.backends [] []
threadpoolctl [] []
locale [] []
configparser [] []
numpy.distutils.command.build_clib [] []
pickle [] []
cffi [] []
importlib [] []
subprocess [] []
sys [] []
numpy.linalg.setup [] []
numpy.f2py.common_rules [] []
copy [] []
time [] []
numpy.lib [] []
numpy.array_api._manipulation_functions [] []
numpy.array_api._constants [] []
numpy.testing._private.noseclasses [] []
numpy.typing.tests.data.pass.warnings_and_errors [] []
numpy.fft [] []
numpy.lib.arraypad [] []
numpy.typing.tests.data.pass.einsumfunc [] []
numpy.f2py.capi_maps [] []
numpy.distutils.line_endings [] []
numpy.distutils.fcompiler.ibm [] []
opt_einsum.sharing [] []
numpy.ma.bench [] []
numpy.typing.setup [] []
numpy.distutils.intelccompiler [] []
opt_einsum.paths [] []
numpy._typing._add_docstring [] []
numpy.version [] []
threading [] []
numpy._pytesttester [] []
numpy.distutils.core [] []
numpy.typing.tests.data.pass.ufunclike [] []
io [] []
numpy.distutils.command.install_headers [] []
numpy.core._asarray [] []
numpy.distutils.npy_pkg_config [] []
shlex [] []
opt_einsum.typing [] []
numpy.distutils.fcompiler.absoft [] []
numbers [] []
numpy.array_api._creation_functions [] []
inspect [] []
PyInstaller [] []
numpy.polynomial.setup [] []
numpy.f2py.use_rules [] []
numpy.f2py [] []
numpy.core.memmap [] []
tokenize [] []
numpy.doc.ufuncs [] []
theano [] []
numpy.testing._private.decorators [] []
numpy.distutils.lib2def [] []
numpy.polynomial.legendre [] []
numpy.core.defchararray [] []
numpy.core.umath_tests [] []
numpy.distutils.command.install_clib [] []
numpy.core.records [] []
numpy.distutils.msvccompiler [] []
winreg [] []
numpy.matrixlib [] []
numpy.fft.setup [] []
numpy.distutils.fcompiler.environment [] []
numpy.f2py.diagnose [] []
numpy.testing._private.extbuild [] []
numpy._distributor_init [] []
numpy.polynomial.laguerre [] []
numpy.typing.tests.data.pass.ndarray_misc [] []
numpy.f2py.__main__ [] []
numpy.lib.arraysetops [] []
mtrand [] []
numpy.distutils._shell_utils [] []
contextlib [] []
numpy.lib.ufunclike [] []
numpy.polynomial._polybase [] []
numpy._typing._dtype_like [] []
numpy.f2py.f2py2e [] []
numpy.lib.twodim_base [] []
numpy.typing.tests.data.pass.lib_version [] []
datetime [] []
ast [] []
numpy._globals [] []
base64 [] []
numpy.f2py.symbolic [] []
numpy.distutils.command.build_ext [] []
numpy.random._examples.cffi.parse [] []
tensorflow [] []
timeit [] []
numpy.matrixlib.defmatrix [] []
numpy.typing.tests.data.pass.ufunc_config [] []
numpy.array_api._elementwise_functions [] []
numpy.compat._inspect [] []
numpy.compat [] []
numpy.ma [] []
numpy.ma.extras [] []
pytest [] []
numpy.typing.tests.data.pass.mod [] []
math [] []
[] []
numpy.distutils.fcompiler.none [] []
numpy.f2py.auxfuncs [] []
numpy.distutils.command.build [] []
numba [] []
numpy.doc [] []
numpy.testing.utils [] []
numpy.distutils.command.sdist [] []
numpy.f2py.crackfortran [] []
numpy.dual [] []
numpy._typing._array_like [] []
numpy.ctypeslib [] []
numpy.random.tests.data [] []
numpy.random [] []
numpy.random._examples.cffi.extending [] []
numpy.distutils.fcompiler.intel [] []
hypothesis [] []
numpy.typing.tests.data.pass.multiarray [] []
numpy.lib.arrayterator [] []
numpy.testing._private.nosetester [] []
urllib [] []
opt_einsum.backends.dispatch [] []
numpy._typing._scalars [] []
numpy.testing._private.parameterized [] []
optparse [] []
numpy.distutils.command.develop [] []
numpy.lib._version [] []
numpy.random.setup [] []
numpy._typing._char_codes [] []

Directories in report

Directory

Metadata section

This sections shows the raw data that is used to produce this report. This is mainly used for further processing and developer debugging.

Fuzzer Calltree file Program data file Coverage file
fuzz_parser fuzzerLogFile-fuzz_parser.data fuzzerLogFile-fuzz_parser.data.yaml all_cov.json

Function call coverage

This section shows a chosen list of functions / methods calls and their relative coverage information. By static analysis of the target project code, all of these function call and their caller information, including the source file or class and line number that initiate the call are captured. Column 1 is the function name of that selected functions or methods. Column 2 of each row indicate if the target function covered by any fuzzer calltree information. Column 3 lists all fuzzers (or no fuzzers at all) that have coered that particular function call dynamically. Column 4 shows list of parent function for the specific function call, while column 5 shows possible blocker functions that make the fuzzers fail to reach the specific functions. Both column 4 and 5 will only show information if none of the fuzzers cover the target function calls.

Function in each files in report

Target sink Callsite location Reached by fuzzer Function call path Covered by fuzzer Possible branch blockers
.eval Not in fuzzer provided call tree []
Parent functions Callpaths
numpy.distutils.misc_util.get_path_from_frame
in numpy.distutils.misc_util:148
Path 1
Path 2
Path 3
Path 4
Path 5
Path 6
Path 7
Path 8
Path 9
Path 10
Path 11
Path 12
Path 13
Path 14
Path 15
Path 16
Path 17
numpy.f2py.f2py2e.buildmodules
in numpy.f2py.f2py2e:366 		Path 1
numpy.f2py.crackfortran
in numpy.f2py.crackfortran:-1 		Path 1
numpy.f2py.crackfortran.myeval
in numpy.f2py.crackfortran:2230 		Path 1
numpy.f2py.auxfuncs.l_or
in numpy.f2py.auxfuncs:606 		Path 1
Path 2
Path 3
numpy.testing._private.parameterized.parameterized.__call__
in numpy.testing._private.parameterized:281 		Path 1
numpy.ma.timer_comparison
in numpy.ma.timer_comparison:-1 		Path 1
0
Blocker function Arguments type Return type Constants touched
numpy.setup.configuration
in numpy.setup:3 		['N/A', 'N/A'] N/A []
numpy.compat.setup.configuration
in numpy.compat.setup:1 		['N/A', 'N/A'] N/A []
numpy.array_api.setup.configuration
in numpy.array_api.setup:1 		['N/A', 'N/A'] N/A []
numpy.ma.setup
in numpy.ma.setup:-1 		[] N/A []
numpy.random.setup.configuration
in numpy.random.setup:9 		['N/A', 'N/A'] N/A []
numpy.testing.setup.configuration
in numpy.testing.setup:3 		['N/A', 'N/A'] N/A []
numpy.f2py.setup
in numpy.f2py.setup:-1 		[] N/A []
numpy.typing.setup.configuration
in numpy.typing.setup:1 		['N/A', 'N/A'] N/A []
numpy.distutils.setup.configuration
in numpy.distutils.setup:2 		['N/A', 'N/A'] N/A []
numpy.matrixlib.setup
in numpy.matrixlib.setup:-1 		[] N/A []
numpy.core.setup.configuration
in numpy.core.setup:464 		['N/A', 'N/A'] N/A []
numpy.lib.setup.configuration
in numpy.lib.setup:1 		['N/A', 'N/A'] N/A []
numpy.polynomial.setup.configuration
in numpy.polynomial.setup:1 		['N/A', 'N/A'] N/A []
numpy.fft.setup.configuration
in numpy.fft.setup:3 		['N/A', 'N/A'] N/A []
numpy.linalg.setup.configuration
in numpy.linalg.setup:5 		['N/A', 'N/A'] N/A []
numpy.distutils.misc_util.default_config_dict
in numpy.distutils.misc_util:2282 		['N/A', 'N/A', 'N/A'] N/A []
numpy._typing.setup.configuration
in numpy._typing.setup:1 		['N/A', 'N/A'] N/A []
numpy.f2py.__main__
in numpy.f2py.__main__:-1 		[] N/A []
numpy.f2py.crackfortran
in numpy.f2py.crackfortran:-1 		[] N/A []
numpy.f2py.crackfortran.getlincoef
in numpy.f2py.crackfortran:2240 		['N/A', 'N/A'] N/A []
numpy.f2py.capi_maps.common_sign2map
in numpy.f2py.capi_maps:860 		['N/A', 'N/A'] N/A []
numpy.f2py.cb_rules
in numpy.f2py.cb_rules:-1 		[] N/A []
numpy.f2py.rules
in numpy.f2py.rules:-1 		[] N/A []
numpy.testing._private.parameterized.parameterized.__call__
in numpy.testing._private.parameterized:281 		['N/A', 'N/A'] N/A []
numpy.ma.timer_comparison
in numpy.ma.timer_comparison:-1 		[] N/A []
.exec Not in fuzzer provided call tree []
Parent functions Callpaths
numpy.testing._private.utils.runstring
in numpy.testing._private.utils:1187 		Path 1
numpy.testing._private.utils.measure
in numpy.testing._private.utils:1454 		Path 1
0
Blocker function Arguments type Return type Constants touched
numpy.testing._private.utils.runstring
in numpy.testing._private.utils:1187 		['N/A', 'N/A'] N/A []
numpy.testing._private.utils.measure
in numpy.testing._private.utils:1454 		['N/A', 'N/A', 'N/A'] N/A []