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

Table of contents

Project overview: networkx
High level conclusions
Reachability and coverage overview
Fuzzers overview
Project functions overview
Fuzzer details
Fuzzer: fuzz_graphml
Call tree
Runtime coverage analysis
Files reached
Fuzzer: fuzz_graph6
Call tree
Runtime coverage analysis
Files reached
Fuzzer: fuzz_sparse6
Call tree
Runtime coverage analysis
Files reached
Analyses and suggestions
Optimal target analysis
Remaining optimal interesting functions
All functions overview
Fuzz engine guidance
/src/fuzz_graphml.py
Dictionary
Fuzzer function priority
/src/fuzz_graph6.py
Dictionary
Fuzzer function priority
/src/fuzz_sparse6.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: 2025-11-16

Project overview: networkx

High level conclusions

Reachability and coverage overview

Functions statically reachable by fuzzers
0.0%
0 / 2177
Cyclomatic complexity statically reachable by fuzzers
0.0%
0 / 8270
Runtime code coverage of functions
22.0%
476 / 2177

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_graphml /src/fuzz_graphml.py 3 4691 1 2 0 6 fuzz_graphml.py
fuzz_graph6 /src/fuzz_graph6.py 3 4689 1 2 0 6 fuzz_graph6.py
fuzz_sparse6 /src/fuzz_sparse6.py 3 4691 1 2 0 6 fuzz_sparse6.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_graphml

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 0 0.0%
gold [1:9] 0 0.0%
yellow [10:29] 0 0.0%
greenyellow [30:49] 0 0.0%
lawngreen 50+ 3 100.%
All colors 3 100

Runtime coverage analysis

Covered functions
777
Functions that are reachable but not covered
3
Reachable functions
3
Percentage of reachable functions covered
0.0%
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_graphml 2

Fuzzer: fuzz_graph6

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 0 0.0%
gold [1:9] 0 0.0%
yellow [10:29] 0 0.0%
greenyellow [30:49] 0 0.0%
lawngreen 50+ 3 100.%
All colors 3 100

Runtime coverage analysis

Covered functions
777
Functions that are reachable but not covered
3
Reachable functions
3
Percentage of reachable functions covered
0.0%
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_graph6 2

Fuzzer: fuzz_sparse6

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 0 0.0%
gold [1:9] 0 0.0%
yellow [10:29] 0 0.0%
greenyellow [30:49] 0 0.0%
lawngreen 50+ 3 100.%
All colors 3 100

Runtime coverage analysis

Covered functions
777
Functions that are reachable but not covered
3
Reachable functions
3
Percentage of reachable functions covered
0.0%
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_sparse6 2

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
networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem networkx.algorithms.approximation.traveling_salesman 6 ['N/A', 'N/A', 'N/A', 'N/A', 'N/A', 'N/A'] 6 0 4 7 6 164 0 532 529
networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation networkx.algorithms.connectivity.edge_augmentation 5 ['N/A', 'N/A', 'N/A', 'N/A', 'N/A'] 6 0 3 8 6 148 190 476 286
networkx.linalg.algebraicconnectivity.fiedler_vector networkx.linalg.algebraicconnectivity 6 ['N/A', 'N/A', 'N/A', 'N/A', 'N/A', 'N/A'] 6 0 1 3 4 124 0 397 255
networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms networkx.algorithms.isomorphism.vf2pp 4 ['N/A', 'N/A', 'N/A', 'N/A'] 3 0 9 9 7 82 2 278 248
networkx.drawing.nx_pylab.draw networkx.drawing.nx_pylab 4 ['N/A', 'N/A', 'N/A', 'N/A'] 4 0 5 4 5 79 9 261 210
networkx.algorithms.matching.max_weight_matching networkx.algorithms.matching 3 ['N/A', 'N/A', 'N/A'] 3 0 14 38 18 72 1 249 177
networkx.algorithms.cycles._min_cycle networkx.algorithms.cycles 3 ['N/A', 'N/A', 'N/A'] 8 0 10 5 5 91 2 304 171

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

Functions statically reachable by fuzzers
7.00%
160 / 2177
Cyclomatic complexity statically reachable by fuzzers
8.0%
661 / 8270

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_graphml.py

Dictionary

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


/src/fuzz_graph6.py

Dictionary

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


/src/fuzz_sparse6.py

Dictionary

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


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
networkx.relabel._relabel_inplace 36 0 0.0% []
networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges 31 0 0.0% []
networkx.algorithms.community.asyn_fluid.asyn_fluidc 59 0 0.0% []
networkx.algorithms.cycles.cycle_basis 32 0 0.0% []
networkx.algorithms.cycles._johnson_cycle_search 32 0 0.0% []
networkx.algorithms.cycles._bounded_cycle_search 32 0 0.0% []
networkx.algorithms.cycles.chordless_cycles 53 0 0.0% []
networkx.algorithms.cycles.recursive_simple_cycles.circuit 38 0 0.0% []
networkx.algorithms.cycles.find_cycle.tailhead 51 2 3.921% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.shortest_paths.unweighted._bidirectional_pred_succ 32 0 0.0% []
networkx.algorithms.shortest_paths.unweighted.predecessor 31 0 0.0% []
networkx.algorithms.shortest_paths.generic.shortest_path 31 0 0.0% []
networkx.algorithms.shortest_paths.generic.shortest_path_length 35 0 0.0% []
networkx.algorithms.regular.k_factor 32 0 0.0% []
networkx.algorithms.centrality.dispersion.dispersion._dispersion 32 0 0.0% []
networkx.algorithms.simple_paths._bidirectional_pred_succ.filter_iter.iterate 31 0 0.0% []
networkx.algorithms.simple_paths._bidirectional_dijkstra.filter_iter.iterate 47 0 0.0% []
networkx.algorithms.connectivity.edge_augmentation.unconstrained_bridge_augmentation 34 0 0.0% []
networkx.algorithms.connectivity.edge_augmentation.weighted_bridge_augmentation 44 0 0.0% []
networkx.algorithms.connectivity.edge_augmentation.greedy_k_edge_augmentation 33 0 0.0% []
networkx.algorithms.cluster.square_clustering.GAdj.__missing__ 35 1 2.857% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.coloring.equitable_coloring.procedure_P 102 0 0.0% []
networkx.algorithms.coloring.equitable_coloring.equitable_color 38 0 0.0% []
networkx.algorithms.centrality.group.group_betweenness_centrality 63 0 0.0% []
networkx.algorithms.centrality.group.prominent_group 47 0 0.0% []
networkx.algorithms.centrality.group._heuristic 40 0 0.0% []
networkx.algorithms.clique.find_cliques 40 1 2.5% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.triads.triadic_census 50 0 0.0% []
networkx.algorithms.triads.triad_type 42 0 0.0% []
networkx.algorithms.distance_regular.intersection_array 33 2 6.060% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.connectivity.connectivity.edge_connectivity 41 0 0.0% []
networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem 35 2 5.714% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.approximation.traveling_salesman.asadpour_atsp 49 0 0.0% []
networkx.algorithms.approximation.traveling_salesman.held_karp_ascent.k_pi 42 0 0.0% []
networkx.algorithms.approximation.traveling_salesman.held_karp_ascent.direction_of_ascent 32 0 0.0% []
networkx.algorithms.approximation.traveling_salesman.held_karp_ascent.find_epsilon 58 0 0.0% []
networkx.algorithms.approximation.traveling_salesman.spanning_tree_distribution.q 31 0 0.0% []
networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp 47 1 2.127% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp 45 0 0.0% []
networkx.algorithms.dag.all_topological_sorts 35 0 0.0% []
networkx.algorithms.connectivity.cuts.minimum_edge_cut 43 0 0.0% []
networkx.algorithms.bipartite.matching.eppstein_matching 32 0 0.0% []
networkx.algorithms.connectivity.kcomponents.k_components 34 0 0.0% []
networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor 42 0 0.0% []
networkx.algorithms.components.strongly_connected.strongly_connected_components 37 1 2.702% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.components.biconnected._biconnected_dfs 48 0 0.0% []
networkx.algorithms.approximation.density._greedy_plus_plus 31 0 0.0% []
networkx.algorithms.approximation.density._fista 40 3 7.5% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.core.onion_layers 32 0 0.0% []
networkx.algorithms.matching.max_weight_matching.addBlossom 59 0 0.0% []
networkx.algorithms.matching.max_weight_matching.expandBlossom._recurse 65 0 0.0% []
networkx.algorithms.matching.max_weight_matching.augmentBlossom._recurse 35 0 0.0% []
networkx.algorithms.matching.max_weight_matching.verifyOptimum 151 0 0.0% []
networkx.algorithms.d_separation.is_d_separator 40 1 2.5% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.d_separation.find_minimal_d_separator 33 1 3.030% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.d_separation.is_minimal_d_separator 37 0 0.0% []
networkx.convert.to_networkx_graph 69 7 10.14% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.connectivity.kcutsets.all_node_cuts 70 0 0.0% []
networkx.algorithms.planarity.LRPlanarity.lr_planarity 43 0 0.0% []
networkx.algorithms.planarity.LRPlanarity.dfs_orientation 34 0 0.0% []
networkx.algorithms.planarity.LRPlanarity.add_constraints 31 0 0.0% []
networkx.algorithms.planarity.PlanarEmbedding.check_structure 31 0 0.0% []
networkx.algorithms.coloring.greedy_coloring._greedy_coloring_with_interchange 65 0 0.0% []
networkx.algorithms.hybrid.kl_connected_subgraph 39 0 0.0% []
networkx.algorithms.smallworld.random_reference 43 3 6.976% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.smallworld.lattice_reference 53 3 5.660% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.similarity.optimize_edit_paths.match_edges 32 0 0.0% []
networkx.algorithms.similarity.optimize_edit_paths.get_edit_paths 91 0 0.0% []
networkx.algorithms.similarity.panther_vector_similarity 32 1 3.125% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.similarity.generate_random_paths 32 0 0.0% []
networkx.algorithms.approximation.kcomponents.k_components 39 0 0.0% []
networkx.convert_matrix.from_pandas_edgelist 42 0 0.0% []
networkx.convert_matrix.to_scipy_sparse_array 39 0 0.0% []
networkx.convert_matrix.to_numpy_array 48 0 0.0% []
networkx.convert_matrix.from_numpy_array 36 0 0.0% []
networkx.algorithms.flow.boykovkolmogorov.boykov_kolmogorov_impl.augment 35 0 0.0% []
networkx.drawing.nx_agraph.from_agraph 34 0 0.0% []
networkx.utils.backends._dispatchable._call_if_any_backends_installed 150 1 0.666% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.utils.backends._dispatchable._convert_arguments 89 0 0.0% []
networkx.utils.backends._dispatchable._convert_graph 34 0 0.0% []
networkx.utils.backends._dispatchable._convert_and_call_for_tests 60 0 0.0% []
networkx.utils.backends._dispatchable._make_doc 55 20 36.36% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.utils.backends._get_from_cache 33 0 0.0% []
networkx.algorithms.flow.shortestaugmentingpath.shortest_augmenting_path_impl 36 0 0.0% []
networkx.algorithms.flow.shortestaugmentingpath.shortest_augmenting_path_impl.relabel 51 0 0.0% []
networkx.readwrite.gexf.GEXFWriter.add_nodes 34 0 0.0% []
networkx.readwrite.gexf.GEXFWriter.add_edges.edge_key_data 61 0 0.0% []
networkx.readwrite.gexf.GEXFWriter.add_attributes 52 0 0.0% []
networkx.readwrite.gexf.GEXFReader.make_graph 56 0 0.0% []
networkx.readwrite.gexf.GEXFReader.add_edge 32 0 0.0% []
networkx.readwrite.multiline_adjlist.generate_multiline_adjlist 38 0 0.0% []
networkx.readwrite.multiline_adjlist.parse_multiline_adjlist 54 1 1.851% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.generators.line.inverse_line_graph 31 0 0.0% []
networkx.generators.line._select_starting_cell 44 0 0.0% []
networkx.readwrite.graphml.GraphMLWriterLxml.add_graph_element 44 0 0.0% []
networkx.readwrite.text.generate_network_text 107 0 0.0% []
networkx.readwrite.text._parse_network_text 96 0 0.0% []
networkx.readwrite.pajek.generate_pajek 35 0 0.0% []
networkx.readwrite.pajek.parse_pajek 67 0 0.0% []
l.values 31 14 45.16% ['fuzz_graph6', 'fuzz_graphml']
networkx.readwrite.gml.parse_gml_lines.tokenize 40 0 0.0% []
networkx.readwrite.gml.parse_gml_lines.parse_kv 39 0 0.0% []
networkx.readwrite.gml.parse_gml_lines.pop_attr 42 0 0.0% []
networkx.readwrite.gml.literal_stringizer.stringize 65 0 0.0% []
networkx.readwrite.gml.generate_gml.stringize 83 0 0.0% []
networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism 34 2 5.882% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.readwrite.sparse6.from_sparse6_bytes.parseData 42 0 0.0% []
networkx.readwrite.edgelist.parse_edgelist 43 1 2.325% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.swap.directed_edge_swap 43 1 2.325% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.swap.double_edge_swap 35 1 2.857% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.swap.connected_double_edge_swap 75 0 0.0% []
networkx.algorithms.distance_measures._extrema_bounding 63 0 0.0% []
networkx.algorithms.distance_measures.resistance_distance 46 1 2.173% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.shortest_paths.weighted._dijkstra_multisource 46 0 0.0% []
networkx.algorithms.shortest_paths.weighted._inner_bellman_ford 44 0 0.0% []
networkx.algorithms.shortest_paths.weighted.goldberg_radzik.topo_sort 31 0 0.0% []
networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra.path 41 0 0.0% []
networkx.algorithms.flow.capacityscaling.capacity_scaling 92 0 0.0% []
networkx.algorithms.approximation.treewidth.treewidth_decomp 31 0 0.0% []
networkx.algorithms.flow.preflowpush.preflow_push_impl.global_relabel 61 0 0.0% []
networkx.algorithms.flow.edmondskarp.edmonds_karp_core.bidirectional_bfs 44 0 0.0% []
networkx.algorithms.flow.dinitz_alg.dinitz_impl.depth_first_search 38 0 0.0% []
networkx.generators.degree_seq.havel_hakimi_graph 38 0 0.0% []
networkx.generators.degree_seq.directed_havel_hakimi_graph 53 0 0.0% []
networkx.algorithms.flow.networksimplex._DataEssentialsAndFunctions.__init__ 33 0 0.0% []
networkx.algorithms.flow.networksimplex.network_simplex 59 0 0.0% []
networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths 47 1 2.127% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.connectivity.stoerwagner.stoer_wagner 43 0 0.0% []
networkx.algorithms.shortest_paths.astar.astar_path.heuristic 39 0 0.0% []
networkx.algorithms.centrality.laplacian.laplacian_centrality 33 0 0.0% []
networkx.algorithms.centrality.current_flow_betweenness.approximate_current_flow_betweenness_centrality 41 1 2.439% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.centrality.katz.katz_centrality 33 1 3.030% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.centrality.closeness.incremental_closeness_centrality 32 0 0.0% []
networkx.algorithms.approximation.connectivity._bidirectional_pred_succ 36 0 0.0% []
networkx.algorithms.link_analysis.hits_alg._hits_python 38 0 0.0% []
networkx.algorithms.link_analysis.pagerank_alg._pagerank_scipy 33 0 0.0% []
networkx.algorithms.tree.branchings.maximum_branching.edmonds_remove_node 32 0 0.0% []
networkx.algorithms.tree.branchings.maximum_branching.edmonds_step_I2 46 0 0.0% []
networkx.algorithms.tree.branchings.maximum_branching.is_root 69 0 0.0% []
networkx.algorithms.tree.mst.kruskal_mst_edges 43 0 0.0% []
networkx.algorithms.tree.mst.prim_mst_edges 65 2 3.076% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.tree.mst.random_spanning_tree.spanning_tree_total_weight 46 7 15.21% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.structuralholes.effective_size.redundancy 38 0 0.0% []
networkx.algorithms.isomorphism.isomorphvf2.GraphMatcher.syntactic_feasibility 51 2 3.921% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.isomorphism.isomorphvf2.DiGraphMatcher.syntactic_feasibility 117 0 0.0% []
networkx.algorithms.isomorphism.isomorphvf2.GMState.__init__ 31 0 0.0% []
networkx.algorithms.isomorphism.isomorphvf2.DiGMState.__init__ 47 0 0.0% []
networkx.algorithms.isomorphism.ismags.ISMAGS.create_aligned_partitions.g_match 33 0 0.0% []
networkx.algorithms.isomorphism.ismags.ISMAGS._map_nodes 75 0 0.0% []
networkx.algorithms.isomorphism.ismags.ISMAGS._process_ordered_pair_partitions._load_next_queue_entry 45 0 0.0% []
networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms 52 0 0.0% []
networkx.algorithms.isomorphism.vf2pp._cut_PT 41 0 0.0% []
networkx.algorithms.isomorphism.vf2pp._restore_Tinout_Di 50 0 0.0% []
networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph 34 1 2.941% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.bipartite.generators.random_graph 33 1 3.030% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.bipartite.link_analysis.birank 47 0 0.0% []
networkx.algorithms.bipartite.edgelist.parse_edgelist 41 1 2.439% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.algorithms.assortativity.neighbor_degree.average_neighbor_degree 35 0 0.0% []
networkx.algorithms.assortativity.connectivity.average_degree_connectivity 32 0 0.0% []
networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos 54 0 0.0% []
networkx.algorithms.planar_drawing.get_canonical_ordering.is_on_outer_face 58 0 0.0% []
networkx.algorithms.summarization.dedensify 34 0 0.0% []
networkx.algorithms.community.local._clauset_greedy_source_expansion 32 0 0.0% []
networkx.algorithms.community.divisive.edge_current_flow_betweenness_partition 39 0 0.0% []
networkx.algorithms.community.louvain._one_level 61 0 0.0% []
networkx.algorithms.community.lukes.lukes_partitioning._concatenate_or_merge 48 0 0.0% []
networkx.algorithms.community.modularity_max._greedy_modularity_communities_generator 86 0 0.0% []
networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities 31 0 0.0% []
networkx.algorithms.graphical.is_digraphical 53 0 0.0% []
networkx.algorithms.sparsifiers.spanner 64 0 0.0% []
networkx.generators.joint_degree_seq.joint_degree_graph 46 0 0.0% []
networkx.generators.joint_degree_seq.directed_joint_degree_graph 78 0 0.0% []
networkx.generators.directed.scale_free_graph._choose_node 49 1 2.040% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.generators.lattice.triangular_lattice_graph 32 0 0.0% []
networkx.generators.lattice.hexagonal_lattice_graph 34 0 0.0% []
networkx.generators.random_graphs.extended_barabasi_albert_graph 55 1 1.818% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.generators.random_graphs.powerlaw_cluster_graph 31 1 3.225% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.generators.community.stochastic_block_model 65 0 0.0% []
networkx.generators.social.les_miserables_graph 256 0 0.0% []
networkx.generators.geometric.geometric_soft_configuration_graph 40 0 0.0% []
networkx.drawing.layout.spring_layout 49 1 2.040% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.drawing.layout._sparse_fruchterman_reingold 41 0 0.0% []
networkx.drawing.layout.multipartite_layout 39 0 0.0% []
networkx.drawing.layout.arf_layout 41 1 2.439% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.drawing.layout.forceatlas2_layout 32 0 0.0% []
networkx.drawing.layout.forceatlas2_layout.estimate_factor 71 0 0.0% []
networkx.drawing.nx_pylab.CurvedArrowTextBase._update_text_pos_angle 48 0 0.0% []
networkx.drawing.nx_pylab.display.build_fancy_arrow 101 0 0.0% []
networkx.drawing.nx_pylab.draw_networkx_nodes 32 2 6.25% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']
networkx.drawing.nx_pylab.FancyArrowFactory.__call__ 44 0 0.0% []
networkx.drawing.nx_pylab.draw_networkx_edges 93 0 0.0% []
networkx.drawing.nx_pylab.draw_networkx_edge_labels 35 0 0.0% []
networkx.drawing.nx_pydot.from_pydot 49 0 0.0% []
networkx.drawing.nx_pydot.to_pydot 35 0 0.0% []
networkx.drawing.nx_latex.to_latex_raw 58 3 5.172% ['fuzz_graph6', 'fuzz_sparse6', 'fuzz_graphml']

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
[] []
networkx.algorithms.centrality.flow_matrix [] []
networkx.algorithms.swap [] []
networkx.drawing [] []
networkx.algorithms.shortest_paths.dense [] []
networkx.algorithms.community.asyn_fluid [] []
networkx.algorithms.bipartite.link_analysis [] []
networkx.generators.interval_graph [] []
networkx.algorithms.non_randomness [] []
networkx.linalg [] []
networkx.algorithms.bipartite.extendability [] []
networkx.classes.function [] []
networkx.generators.classic [] []
networkx.algorithms.components.attracting [] []
networkx.algorithms.asteroidal [] []
networkx.lazy_imports [] []
networkx.algorithms.centrality.degree_alg [] []
networkx.algorithms.matching [] []
networkx.algorithms.chordal [] []
networkx.algorithms.reciprocity [] []
networkx.linalg.graphmatrix [] []
networkx.generators.triads [] []
networkx.algorithms.assortativity [] []
matplotlib [] []
networkx.algorithms.walks [] []
networkx.algorithms.approximation.traveling_salesman [] []
networkx.linalg.modularitymatrix [] []
networkx.generators.atlas [] []
networkx.algorithms.shortest_paths [] []
networkx.algorithms.shortest_paths.astar [] []
networkx.algorithms.traversal.beamsearch [] []
networkx.algorithms.perfect_graph [] []
networkx.algorithms.centrality.subgraph_alg [] []
networkx.linalg.attrmatrix [] []
networkx.algorithms.connectivity.disjoint_paths [] []
networkx.linalg.algebraicconnectivity [] []
sympy [] []
bisect [] []
networkx.algorithms.distance_regular [] []
networkx.readwrite.json_graph.node_link [] []
networkx.algorithms.flow.networksimplex [] []
networkx.algorithms.operators.unary [] []
networkx.algorithms.similarity [] []
networkx.algorithms.coloring [] []
networkx.readwrite.p2g [] []
networkx.generators.stochastic [] []
networkx.generators.degree_seq [] []
networkx.algorithms.dominance [] []
networkx.algorithms.traversal.depth_first_search [] []
networkx.algorithms.bipartite [] []
re [] []
networkx.linalg.spectrum [] []
networkx.algorithms.components [] []
networkx.algorithms.hybrid [] []
networkx.readwrite.graphml [] []
ast [] []
networkx.convert_matrix [] []
networkx.algorithms.approximation.clique [] []
networkx.readwrite.graph6 [] []
networkx.algorithms.flow.boykovkolmogorov [] []
networkx.algorithms.centrality.harmonic [] []
networkx.linalg.laplacianmatrix [] []
scipy [] []
networkx.algorithms.dominating [] []
networkx.algorithms.isomorphism.tree_isomorphism [] []
networkx.algorithms.centrality.closeness [] []
networkx.algorithms.moral [] []
networkx.algorithms.shortest_paths.weighted [] []
networkx.algorithms.flow.dinitz_alg [] []
networkx.algorithms.tree.operations [] []
networkx.algorithms.wiener [] []
networkx.generators.random_clustered [] []
networkx.generators.mycielski [] []
queue [] []
networkx.algorithms.link_analysis [] []
importlib [] []
networkx.classes.filters [] []
networkx.drawing.nx_pydot [] []
networkx.algorithms.bipartite.generators [] []
networkx.algorithms.graphical [] []
networkx [] []
itertools [] []
collections [] []
networkx.algorithms.connectivity [] []
networkx.algorithms.connectivity.edge_kcomponents [] []
networkx.algorithms.centrality.laplacian [] []
networkx.algorithms.community.local [] []
random [] []
networkx.utils.rcm [] []
networkx.algorithms.flow.capacityscaling [] []
functools [] []
networkx.algorithms.bipartite.projection [] []
networkx.algorithms.centrality.trophic [] []
io [] []
shlex [] []
networkx.readwrite.pajek [] []
networkx.algorithms.connectivity.kcomponents [] []
math [] []
networkx.algorithms.tree.branchings [] []
networkx.algorithms.broadcasting [] []
networkx.algorithms.assortativity.correlation [] []
networkx.algorithms.flow.preflowpush [] []
lxml [] []
networkx.algorithms.connectivity.edge_augmentation [] []
networkx.linalg.bethehessianmatrix [] []
networkx.algorithms.centrality.betweenness_subset [] []
networkx.algorithms.community.leiden [] []
networkx.algorithms.approximation.clustering_coefficient [] []
networkx.generators.random_graphs [] []
networkx.algorithms.covering [] []
networkx.algorithms.cluster [] []
networkx.readwrite.leda [] []
networkx.algorithms.connectivity.cuts [] []
networkx.algorithms.tree.recognition [] []
networkx.algorithms.approximation.dominating_set [] []
networkx.algorithms.community.community_utils [] []
networkx.algorithms.traversal [] []
networkx.readwrite.sparse6 [] []
networkx.algorithms.community.quality [] []
warnings [] []
pydot [] []
networkx.generators.intersection [] []
networkx.algorithms.core [] []
networkx.readwrite.json_graph [] []
networkx.algorithms.graph_hashing [] []
dataclasses [] []
networkx.generators.duplication [] []
networkx.algorithms.assortativity.neighbor_degree [] []
networkx.algorithms.flow [] []
networkx.algorithms.approximation.ramsey [] []
logging [] []
networkx.algorithms.community.kernighan_lin [] []
networkx.algorithms.community.kclique [] []
networkx.algorithms.richclub [] []
xml [] []
networkx.algorithms.centrality.reaching [] []
networkx.algorithms.isomorphism [] []
networkx.algorithms.operators.all [] []
networkx.generators.joint_degree_seq [] []
networkx.algorithms.flow.utils [] []
networkx.generators.time_series [] []
networkx.algorithms.flow.edmondskarp [] []
networkx.generators.sudoku [] []
networkx.algorithms.euler [] []
networkx.algorithms.regular [] []
networkx.readwrite.gexf [] []
networkx.algorithms.coloring.equitable_coloring [] []
networkx.readwrite.json_graph.adjacency [] []
networkx.algorithms.connectivity.utils [] []
networkx.algorithms.approximation.matching [] []
networkx.algorithms.centrality.group [] []
networkx.classes.multigraph [] []
networkx.algorithms.centrality.percolation [] []
networkx.algorithms.community.modularity_max [] []
networkx.algorithms.approximation.steinertree [] []
networkx.algorithms.coloring.greedy_coloring [] []
networkx.algorithms.isomorphism.vf2pp [] []
networkx.algorithms.community.label_propagation [] []
networkx.generators.expanders [] []
networkx.utils.mapped_queue [] []
networkx.algorithms.flow.shortestaugmentingpath [] []
pygraphviz [] []
networkx.algorithms.simple_paths [] []
networkx.classes.digraph [] []
networkx.algorithms.minors.contraction [] []
networkx.readwrite.adjlist [] []
networkx.drawing.nx_latex [] []
networkx.drawing.layout [] []
networkx.algorithms.tree.decomposition [] []
networkx.relabel [] []
networkx.algorithms.shortest_paths.generic [] []
networkx.algorithms.tournament [] []
time [] []
tempfile [] []
networkx.algorithms.mis [] []
[] []
networkx.algorithms.centrality.dispersion [] []
networkx.algorithms.community.centrality [] []
PIL [] []
networkx.algorithms.cycles [] []
networkx.readwrite.json_graph.cytoscape [] []
networkx.generators [] []
networkx.algorithms.link_analysis.hits_alg [] []
networkx.algorithms [] []
networkx.algorithms.threshold [] []
networkx.algorithms.traversal.breadth_first_search [] []
networkx.algorithms.isomorphism.ismags [] []
networkx.algorithms.approximation [] []
networkx.algorithms.centrality.current_flow_betweenness [] []
atheris [] []
networkx.algorithms.flow.mincost [] []
networkx.readwrite.gml [] []
...fuzz_graphml ['fuzz_graphml'] []
networkx.utils.union_find [] []
networkx.algorithms.bipartite.centrality [] []
hashlib [] []
networkx.algorithms.isomorphism.vf2userfunc [] []
networkx.generators.internet_as_graphs [] []
networkx.utils.misc [] []
os [] []
networkx.algorithms.smetric [] []
networkx.algorithms.voronoi [] []
networkx.algorithms.components.connected [] []
networkx.algorithms.planar_drawing [] []
networkx.algorithms.community [] []
...fuzz_sparse6 ['fuzz_sparse6'] []
networkx.algorithms.community.divisive [] []
networkx.algorithms.dag [] []
networkx.algorithms.time_dependent [] []
numpy [] []
networkx.algorithms.bipartite.matching [] []
networkx.algorithms.link_prediction [] []
networkx.algorithms.bipartite.basic [] []
networkx.algorithms.assortativity.connectivity [] []
networkx.classes.coreviews [] []
networkx.classes.multidigraph [] []
networkx.algorithms.bipartite.cluster [] []
networkx.algorithms.operators.product [] []
networkx.exception [] []
networkx.algorithms.chains [] []
networkx.generators.small [] []
networkx.algorithms.traversal.edgedfs [] []
networkx.algorithms.tree [] []
networkx.classes [] []
networkx.algorithms.d_separation [] []
pandas [] []
networkx.algorithms.centrality.voterank_alg [] []
gzip [] []
networkx.utils.backends [] []
types [] []
networkx.algorithms.summarization [] []
networkx.readwrite.text [] []
networkx.generators.social [] []
networkx.algorithms.approximation.maxcut [] []
networkx.algorithms.planarity [] []
networkx.algorithms.distance_measures [] []
networkx.algorithms.components.strongly_connected [] []
networkx.algorithms.centrality.betweenness [] []
networkx.algorithms.traversal.edgebfs [] []
networkx.algorithms.bipartite.edgelist [] []
networkx.algorithms.hierarchy [] []
networkx.generators.geometric [] []
networkx.algorithms.tree.mst [] []
networkx.algorithms.smallworld [] []
networkx.algorithms.node_classification [] []
networkx.algorithms.isomorphism.temporalisomorphvf2 [] []
networkx.algorithms.centrality.eigenvector [] []
networkx.algorithms.structuralholes [] []
networkx.algorithms.approximation.vertex_cover [] []
networkx.generators.line [] []
networkx.algorithms.centrality.second_order [] []
networkx.algorithms.components.biconnected [] []
networkx.algorithms.tree.coding [] []
networkx.algorithms.operators [] []
networkx.algorithms.polynomials [] []
networkx.algorithms.triads [] []
heapq [] []
operator [] []
networkx.classes.reportviews [] []
networkx.algorithms.assortativity.mixing [] []
networkx.algorithms.community.louvain [] []
networkx.classes.graphviews [] []
networkx.algorithms.isomorphism.isomorphvf2 [] []
networkx.algorithms.minors [] []
networkx.algorithms.shortest_paths.unweighted [] []
networkx.algorithms.bipartite.covering [] []
networkx.algorithms.operators.binary [] []
networkx.generators.lattice [] []
networkx.classes.graph [] []
networkx.algorithms.centrality.current_flow_closeness [] []
networkx.generators.directed [] []
networkx.generators.trees [] []
networkx.drawing.nx_pylab [] []
...fuzz_graph6 ['fuzz_graph6'] []
networkx.algorithms.cuts [] []
networkx.drawing.nx_agraph [] []
networkx.conftest [] []
inspect [] []
networkx.algorithms.approximation.density [] []
networkx.algorithms.vitality [] []
networkx.algorithms.approximation.treewidth [] []
networkx.algorithms.bipartite.spectral [] []
networkx.utils.heaps [] []
locale [] []
networkx.utils.decorators [] []
networkx.algorithms.sparsifiers [] []
networkx.algorithms.flow.gomory_hu [] []
networkx.utils [] []
networkx.algorithms.components.semiconnected [] []
networkx.algorithms.efficiency_measures [] []
networkx.algorithms.centrality.katz [] []
networkx.algorithms.components.weakly_connected [] []
networkx.algorithms.bipartite.redundancy [] []
copy [] []
networkx.algorithms.isomorphism.matchhelpers [] []
networkx.generators.cographs [] []
networkx.algorithms.boundary [] []
networkx.algorithms.connectivity.kcutsets [] []
networkx.algorithms.approximation.distance_measures [] []
networkx.generators.community [] []
networkx.algorithms.communicability_alg [] []
networkx.readwrite [] []
networkx.algorithms.assortativity.pairs [] []
networkx.algorithms.approximation.kcomponents [] []
networkx.algorithms.tree.distance_measures [] []
networkx.utils.configs [] []
networkx.algorithms.connectivity.stoerwagner [] []
networkx.algorithms.lowest_common_ancestors [] []
networkx.readwrite.json_graph.tree [] []
networkx.algorithms.isomorphism.isomorph [] []
networkx.algorithms.flow.maxflow [] []
networkx.algorithms.bridges [] []
networkx.readwrite.multiline_adjlist [] []
sys [] []
networkx.algorithms.approximation.connectivity [] []
networkx.generators.nonisomorphic_trees [] []
networkx.algorithms.isolate [] []
networkx.convert [] []
networkx.algorithms.clique [] []
networkx.utils.random_sequence [] []
networkx.algorithms.link_analysis.pagerank_alg [] []
networkx.algorithms.centrality.current_flow_betweenness_subset [] []
networkx.algorithms.bipartite.matrix [] []
networkx.generators.ego [] []
networkx.algorithms.connectivity.connectivity [] []
pytest [] []
networkx.algorithms.centrality [] []
networkx.generators.spectral_graph_forge [] []
networkx.algorithms.community.lukes [] []
networkx.readwrite.edgelist [] []
networkx.generators.harary_graph [] []
networkx.algorithms.centrality.load [] []

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_graphml fuzzerLogFile-fuzz_graphml.data fuzzerLogFile-fuzz_graphml.data.yaml all_cov.json
fuzz_graph6 fuzzerLogFile-fuzz_graph6.data fuzzerLogFile-fuzz_graph6.data.yaml all_cov.json
fuzz_sparse6 fuzzerLogFile-fuzz_sparse6.data fuzzerLogFile-fuzz_sparse6.data.yaml all_cov.json