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

Table of contents

Project overview: dask
High level conclusions
Reachability and coverage overview
Fuzzers overview
Project functions overview
Fuzzer details
Fuzzer: fuzz_fuse
Call tree
Fuzz blockers
Runtime coverage analysis
Files reached
Analyses and suggestions
Optimal target analysis
Remaining optimal interesting functions
All functions overview
Fuzz engine guidance
/src/fuzz_fuse.py
Dictionary
Fuzzer function priority
Runtime coverage analysis
Complex functions with low coverage
Files and Directories in report
Files in report
Directories in report
Metadata section
Report generation date: 2025-07-01

Project overview: dask

High level conclusions

Reachability and coverage overview

Functions statically reachable by fuzzers
0.0%
13 / 4224
Cyclomatic complexity statically reachable by fuzzers
1.0%
74 / 14708
Runtime code coverage of functions
4.0%
153 / 4224

Warning: The number of runtime covered functions are larger than the number of reachable functions. This means that Fuzz Introspector found there are more functions covered at runtime than what is considered reachable based on the static analysis. This is a limitation in the analysis as anything covered at runtime is by definition reachable by the fuzzers.
This is likely due to a limitation in the static analysis. In this case, the count of functions covered at runtime is the true value, which means this is what should be considered "achieved" by the fuzzer.

Use the project functions table below to query all functions that were not covered at runtime.

Fuzzers overview

Fuzzer Fuzzer filename Functions Reached Functions unreached Fuzzer depth Files reached Basic blocks reached Cyclomatic complexity Details
fuzz_fuse /src/fuzz_fuse.py 193 7868 4 6 89 611 fuzz_fuse.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 Runtime reached by Fuzzers Combined 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_fuse

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 0 129 43.1%
gold [1:9] 0 0.0%
yellow [10:29] 0 0.0%
greenyellow [30:49] 0 0.0%
lawngreen 50+ 170 56.8%
All colors 299 100

Fuzz blockers

The following nodes represent call sites where fuzz blockers occur.

Amount of callsites blocked Calltree index Parent function Callsite Largest blocked function
59 110 dask.utils.key_split call site: 00110 dask.utils.key_split
18 85 dask.optimization.fuse call site: 00085 dask.core.subs
10 242 dask.optimization.default_fused_keys_renamer._enforce_max_key_limit call site: 00242 dask.utils.key_split
6 22 dask.optimization.fuse call site: 00022 dask.core.flatten
6 56 dask.optimization.fuse call site: 00056 .len
4 281 dask.optimization.default_fused_linear_keys_renamer call site: 00281 dask.utils.key_split
3 40 dask.core.keys_in_tasks call site: 00040 work.extend
3 49 dask.core.keys_in_tasks call site: 00049 dask.core.get_dependencies
3 229 dask.utils.key_split call site: 00229 word.isalpha
3 254 dask.optimization.fuse_linear call site: 00254 dask.core.flatten
2 33 dask.optimization.fuse call site: 00033 dask.config.get
2 75 dask.core.subs call site: 00075 dask.core.subs

Runtime coverage analysis

Covered functions
203
Functions that are reachable but not covered
180
Reachable functions
193
Percentage of reachable functions covered
6.74%
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_fuse 12
dask.optimization 52
dask.config 2
dask.core 13
dask.utils 124

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
dask.dataframe.dask_expr._collection.to_numeric dask.dataframe.dask_expr._collection 4 ['N/A', 'N/A', 'N/A', 'N/A'] 45 0 1 5 5 1301 0 4228 3684
dask.array.creation.diag dask.array.creation 2 ['N/A', 'N/A'] 50 0 0 11 7 1132 2 3701 440
dask.base.visualize_dsk dask.base 9 ['N/A', 'N/A', 'N/A', 'N/A', 'N/A', 'N/A', 'N/A', 'N/A', 'N/A'] 6 0 0 19 11 290 1 926 378

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

Functions statically reachable by fuzzers
9.0%
390 / 4224
Cyclomatic complexity statically reachable by fuzzers
11.0%
1595 / 14708

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 Runtime reached by Fuzzers Combined 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

Fuzz engine guidance

This sections provides heuristics that can be used as input to a fuzz engine when running a given fuzz target. The current focus is on providing input that is usable by libFuzzer.

/src/fuzz_fuse.py

Dictionary

Use this with the libFuzzer -dict=DICT.file flag


Fuzzer function priority

Use one of these functions as input to libfuzzer with flag: -focus_function name 

-focus_function=['dask.utils.key_split', 'dask.optimization.fuse', 'dask.optimization.default_fused_keys_renamer._enforce_max_key_limit', 'dask.optimization.default_fused_linear_keys_renamer', 'dask.core.keys_in_tasks', 'dask.optimization.fuse_linear']

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
dask.local.start_state_from_dask 52 1 1.923% ['fuzz_fuse']
dask.local.get_async.fire_tasks 44 3 6.818% ['fuzz_fuse']
dask.order.order 68 0 0.0% []
dask.order.order.add_to_result 37 0 0.0% []
dask.order.order.process_runnables 74 0 0.0% []
dask.order.order.path_pop 48 0 0.0% []
dask.order._connecting_to_roots 55 0 0.0% []
dask.order.diagnostics 31 0 0.0% []
dask._task_spec.convert_legacy_task 31 0 0.0% []
dask._task_spec.fuse_linear_task_spec 48 0 0.0% []
dask.core._toposort 53 0 0.0% []
toolz.pluck 105 28 26.66% ['fuzz_fuse']
itertools.cycle 64 19 29.68% ['fuzz_fuse']
dask.utils._derived_from 36 0 0.0% []
dask.highlevelgraph.to_graphviz 63 0 0.0% []
res.map_partitions 144 66 45.83% ['fuzz_fuse']
dask.delayed.unpack_collections 75 12 16.0% ['fuzz_fuse']
dask.delayed.to_task_dask 34 0 0.0% []
dask.base.visualize_dsk 38 0 0.0% []
dask.base.visualize_dsk.label 32 0 0.0% []
dask.base.get_scheduler 50 0 0.0% []
data.append 487 223 45.79% ['fuzz_fuse']
packaging.version.parse 175 21 12.0% ['fuzz_fuse']
yaml.dump 47 22 46.80% ['fuzz_fuse']
dask._expr.Expr.rewrite 42 0 0.0% []
dask._expr.Expr.simplify_once 35 0 0.0% []
dask._expr.Expr._substitute 32 0 0.0% []
dask._expr.Expr._to_graphviz 39 0 0.0% []
pickle.load 569 156 27.41% ['fuzz_fuse']
p.add_layout 188 102 54.25% ['fuzz_fuse']
b.compute 39 14 35.89% ['fuzz_fuse']
constructor.from_dict 480 203 42.29% ['fuzz_fuse']

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
[] []
scipy [] []
dask.dataframe.dask_expr._merge [] []
dask.dataframe.extensions [] []
dask.array._array_expr._slicing [] []
dask [] []
dask.dataframe.io.hdf [] []
dask.array.reductions [] []
dask.dataframe.rolling [] []
dask.array._array_expr._map_blocks [] []
dask.dataframe.api [] []
sparse [] []
dask.array.linalg [] []
dask.cache [] []
dask.array.routines [] []
traceback [] []
dask.dataframe.dask_expr._indexing [] []
atexit [] []
dask.dataframe.dask_expr.diagnostics._analyze_plugin [] []
sys [] []
dask.dataframe.dask_expr.io._delayed [] []
IPython [] []
dask.array.rechunk [] []
dask.utils_test [] []
dask.array._array_expr._shuffle [] []
dask.dataframe._pyarrow_compat [] []
dask.typing [] []
base64 [] []
dask.array._array_expr._collection [] []
dask.array._array_expr._reductions [] []
binascii [] []
dask.diagnostics.progress [] []
dask.dataframe.dask_expr.diagnostics._analyze [] []
dask.array._array_expr._expr [] []
json [] []
dask.dataframe.utils [] []
mimesis [] []
glob [] []
timeit [] []
dask.bag.avro [] []
dask.layers [] []
dask.array [] []
decimal [] []
dask.dataframe.dask_expr._util [] []
dask.callbacks [] []
dask.dataframe [] []
dask.array._array_expr._ufunc [] []
dask.dataframe.io.utils [] []
dask.context [] []
dask.dataframe.dask_expr._rolling [] []
dask.highlevelgraph [] []
dataclasses [] []
dask.dataframe.dask_expr._dummies [] []
datetime [] []
dask.dataframe.dask_expr.datasets [] []
psutil [] []
dask._collections [] []
typing_extensions [] []
dask.dataframe.hyperloglog [] []
dask.array.ma [] []
dask.bag [] []
distributed [] []
dask.array._array_expr.random [] []
dask.delayed [] []
dask.dataframe.dask_expr._merge_asof [] []
collections [] []
dask.dataframe.dask_expr._categorical [] []
dask.array.lib [] []
subprocess [] []
dask.rewrite [] []
os [] []
dask.array._array_expr._utils [] []
dask.array.ufunc [] []
dask.array.chunk_types [] []
tiledb [] []
dask.array.image [] []
dask.dataframe.dask_expr._collection [] []
dask.dataframe.dask_expr.io.bag [] []
zarr [] []
dask.array._shuffle [] []
dask.dataframe.backends [] []
dask.dataframe.io.demo [] []
dask.dataframe.dask_expr.diagnostics [] []
dask.dataframe.indexing [] []
gc [] []
hashlib [] []
pytest [] []
html [] []
types [] []
dask.array.blockwise [] []
dask.dataframe.tseries [] []
ast [] []
copy [] []
importlib_metadata [] []
mmh3 [] []
dask.datasets [] []
multiprocessing [] []
dask.bytes.core [] []
dask.array._array_expr [] []
dask._compatibility [] []
math [] []
dask.backends [] []
fsspec [] []
dask.dataframe.dask_expr._groupby [] []
atheris [] []
packaging [] []
dask.array._array_expr._overlap [] []
dask.dataframe.groupby [] []
fastavro [] []
concurrent [] []
ipywidgets [] []
dask.dataframe._compat [] []
dask.widgets.widgets [] []
dask.array._array_expr._creation [] []
inspect [] []
dask.dataframe.dask_expr._describe [] []
dask.array.chunk [] []
dask.dataframe.io.parquet.utils [] []
...fuzz_fuse ['fuzz_fuse'] []
dask.dataframe.io.parquet.arrow [] []
dask.array.gufunc [] []
dask.dataframe.multi [] []
dask.sizeof [] []
dask.bag.core [] []
dask.diagnostics [] []
toolz [] []
dask.blockwise [] []
dask.graph_manipulation [] []
dask.dataframe.dask_expr._backends [] []
dask.array.svg [] []
dask.array.overlap [] []
dask.dataframe._dtypes [] []
pickle [] []
dask.__main__ [] []
importlib [] []
contextvars [] []
cityhash [] []
dask.dataframe.dispatch [] []
bottleneck [] []
dask.dataframe.partitionquantiles [] []
cupy [] []
dask.dataframe.io.io [] []
dask.bytes.utils [] []
dask.base [] []
logging [] []
dask.array.fft [] []
dask.diagnostics.profile [] []
dask.dataframe.dask_expr._quantiles [] []
random [] []
dask.diagnostics.profile_visualize [] []
dask.array.percentile [] []
dask.dataframe.io [] []
dask.array.utils [] []
dask.array.lib.stride_tricks [] []
dask.dataframe.accessor [] []
dask.tokenize [] []
tempfile [] []
bokeh [] []
dask.dataframe.dask_expr._shuffle [] []
dask.dataframe.dask_expr.diagnostics._explain [] []
dask.dataframe.core [] []
dask.bytes [] []
webbrowser [] []
dask.array.slicing [] []
uuid [] []
dask.dataframe.dask_expr._accessor [] []
dask.array._array_expr._backends [] []
zipfile [] []
pyarrow [] []
dask.dataframe.dask_expr._str_accessor [] []
cloudpickle [] []
dask.dataframe.dask_expr.io.parquet [] []
dask.dataframe.categorical [] []
dask.array.backends [] []
dask.dataframe.dask_expr.io.records [] []
dask.dataframe.io.parquet.core [] []
dask.array.creation [] []
matplotlib [] []
dask.dataframe.dask_expr._interchange [] []
dask._task_spec [] []
tlz [] []
dask.dataframe.io.csv [] []
dask.dataframe.dask_expr._datetime [] []
dask.dataframe.io.parquet [] []
dask.task_spec [] []
dask.array.optimization [] []
dask.dataframe.dask_expr._version [] []
dask.array._reductions_generic [] []
dask.utils ['fuzz_fuse'] []
platform [] []
pandas [] []
dask.dataframe.tseries.resample [] []
dask.dataframe.methods [] []
dask._version [] []
dask.dataframe.dask_expr._quantile [] []
dask.core ['fuzz_fuse'] []
dask.array.dispatch [] []
dask.bag.chunk [] []
dask.dataframe.io.sql [] []
dask.dataframe.dask_expr._repartition [] []
dask.bag.utils [] []
dask.array._array_expr._gufunc [] []
bisect [] []
dask._dispatch [] []
dask.array.tiledb_io [] []
dask.array.stats [] []
weakref [] []
dask.dot [] []
cachey [] []
textwrap [] []
dask.array.numpy_compat [] []
copyreg [] []
warnings [] []
dask.dataframe.dask_expr.io [] []
[] []
queue [] []
dask.dataframe.dask_expr._concat [] []
partd [] []
jinja2 [] []
dask._expr [] []
dask.array._array_expr._blockwise [] []
dask.dataframe.dask_expr [] []
dask.cli [] []
functools [] []
xxhash [] []
pathlib [] []
dask.dataframe.io.orc.core [] []
dask.array.api [] []
h5py [] []
dask.array.cupy_entry_point [] []
dask.widgets [] []
click [] []
dask.threaded [] []
dask.order [] []
dask.system [] []
dask.optimization ['fuzz_fuse'] []
dask.array.reshape [] []
yaml [] []
dask.array.wrap [] []
itertools [] []
contextlib [] []
shutil [] []
time [] []
dask.array.einsumfuncs [] []
fnmatch [] []
dask.config ['fuzz_fuse'] []
dask.dataframe.io.json [] []
dask_cudf [] []
dask.dataframe.shuffle [] []
dask.ml [] []
sqlalchemy [] []
dask.array.core [] []
dask.dataframe.dask_expr._cumulative [] []
operator [] []
dask.dataframe.dask_expr._expr [] []
dask.hashing [] []
dask.array.random [] []
io [] []
numpy [] []
re [] []
codecs [] []
dask.dataframe.io.orc [] []
dask.dataframe.io.orc.arrow [] []
dask.array._array_expr._io [] []
builtins [] []
dask.distributed [] []
dask.dataframe.io.orc.utils [] []
cupyx [] []
dask.dataframe.dask_expr.io.io [] []
heapq [] []
dask.local [] []
dask.multiprocessing [] []
dask.dataframe._pyarrow [] []
typing [] []
dask.conftest [] []
threading [] []
dask.dataframe.dask_expr._reductions [] []
dask.bag.text [] []
crick [] []
dask.bag.random [] []
dask.array._array_expr._rechunk [] []

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_fuse fuzzerLogFile-fuzz_fuse.data fuzzerLogFile-fuzz_fuse.data.yaml all_cov.json