McCabe++ (mcpp): cyclomatic complexity and other vulnerability-related code metrics
Project description
McCabe++ (mcpp)
mcpp
measures typical code complexity metrics like McCabe's cyclomatic
complexity.
The goal of this project is to provide a re-usable script to analyze C/C++ source code and extract complexity metrics from it. The implemented metrics are taken from the paper
LEOPARD: Identifying Vulnerable Code for Vulnerability Assessment through Program Metrics
This tool is released as part of our research in vulnerability discovery and has been used in our paper
SoK: Where to Fuzz? Assessing Target Selection Methods in Directed Fuzzing"
See also the corresponding repo.
Complexity Metrics
Dimension | ID | Metric Description |
---|---|---|
CD1: Function | C1 | cyclomatic complexity |
CD2: Loop Structures | C2 | number of loops |
C3 | number of nested loops | |
C4 | maximum nesting level of loops |
Vulnerability Metrics
Dimension | ID | Metric Description |
---|---|---|
VD1: Dependency | V1 | number of parameter variables |
V2 | number of variables as parameters for callee function | |
VD2: Pointers | V3 | number of pointer arithmetic |
V4 | number of variables involved in pointer arithmetic | |
V5 | maximum number of pointer arithmetic operations a variable is involved in | |
VD3: Control Structures | V6 | number of nested control structures |
V7 | maximum nesting level of control structures | |
V8 | maximum number of control-dependent control structures | |
V9 | maximum number of data-dependent control structures | |
V10 | number of if structures without else | |
V11 | number of variables involved in control predicates |
Setup
Build a docker container which performs the setup automatically or run the installation on your local machine:
pip install .
Note: It is recommended to install packages in virtual environments.
Usage
From Python
Simply import mcpp
and then use the extract function (or one of its variants).
from pathlib import Path
from mcpp import extract
input_dir = Path("some/dir")
in_files = list(input_dir.glob("**/*.c"))
result = extract(in_files)
# to extract only a subset of the metrics
result = extract(in_files, ["V1", "C3"])
# full list of metrics:
from mcpp import METRICS
print(list(METRICS.keys()))
CLI
Configuration parameters can be changed in config.yaml
or directly on the CLI
with e.g. mcpp paths.out_root=some/dir
.
Using all defaults:
mcpp # with default params like input directory, see config.yaml
Changing params from command line:
mcpp in_path=/some/dir/single_source out_path=single_source_metrics.json
mcpp metrics=\[C1,C2,V4\]
Or by passing a changed config.yaml
:
-cp
(config_path) specifies the absolute path to the directory where the config file is located-cn
(config_name) specifies the name of the config file
mcpp -cp /some/other/dir -cn myconfig.yaml
Try out the example:
mcpp in_path=examples/data/source paths.out_root=examples/data-out
cat examples/data-out/complexity.json
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