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GitHub - nico-kunz/pythonJaRvis.github.io

Dataset and Ground truth

The micro-benchmark and macro-benchmark are provided in dataset and ground_truth directory.

Getting Jarvis to run

Prerequisites:

  • Python = 3.8
  • PyCG: tool/PyCG
  • Jarvis: tool/Jarvis

run jarvis_cli.py.

Jarvis usage:

$ python3 tool/Jarvis/jarvis_cli.py [module_path1 module_path2 module_path3...] [--package] [--decy] [-o output_path]

Jarvis help:

$ python3 tool/Jarvis/jarvis_cli.py -h
  usage: jarvis_cli.py [-h] [--package PACKAGE] [--decy] [--precision]
                       [--moduleEntry [MODULEENTRY ...]]
                       [--operation {call-graph,key-error}] [-o OUTPUT]
                       [module ...]

  positional arguments:
    module                modules to be processed, which are also 'Demands' in D.W. mode 

  options:
    -h, --help            show this help message and exit
    --package PACKAGE     Package containing the code to be analyzed
    --decy                whether analyze the dependencies
    --precision           whether flow-sensitive
    --entry-point [MODULEENTRY ...]
                          Entry functions to be processed
    -o OUTPUT, --output OUTPUT
                          Output call graph path

Example 1: analyze bpytop.py in E.A. mode.

$ python3 tool/Jarvis/jarvis_cli.py dataset/macro_benchmark/pj/bpytop/bpytop.py --package dataset/macro_benchmark/pj/bpytop -o jarvis.json

Example 2: analyze bpytop.py in A.W. mode. Note we should prepare all the dependencies in the virtual environment.

# create virtualenv environment
$ virtualenv venv python=python3.8
# install Dependencies in virtualenv environment
$ python3 -m pip install psutil
# run jarvis
$ python3 tool/Jarvis/jarvis_cli.py dataset/macro_benchmark/pj/bpytop/bpytop.py --package dataset/macro_benchmark/pj/bpytop --decy -o jarvis.json

Evaluation

RQ1 and RQ2 Setup

cd to the root directory of the unzipped files.

# 1. run micro_benchmark
$ ./reproducing_RQ12_setup/micro_benchmark/test_All.sh
# 2. run macro_benchmark
$ ./reproducing_RQ12_setup/macro_benchmark/pycg_EA.sh
#     PyCG iterates once
$ ./reproducing_RQ12_setup/macro_benchmark/pycg_EW.sh 1
#     PyCG iterates twice
$ ./reproducing_RQ12_setup/macro_benchmark/pycg_EW.sh 2
#     PyCG iterates to convergence 
$ ./reproducing_RQ12_setup/macro_benchmark/pycg_EW.sh
$ ./reproducing_RQ12_setup/macro_benchmark/jarvis_AA.sh
$ ./reproducing_RQ12_setup/macro_benchmark/jarvis_EA.sh
$ ./reproducing_RQ12_setup/macro_benchmark/jarvis_AW.sh

RQ1. Scalability Evaluation

Scalability results

Run

$ python3 ./reproducing_RQ1/gen_table.py

The results are shown below:

scalability

AGs and FTGs

Run

$ pip3 install matplotlib
$ pip3 install numpy
$ python3 ./reproducing_RQ1/FTG/plot.py

The generated graphs are pycg-ag.pdf, pycg-change-ag.pdf and jarvis-ftg.pdf, where they represents Fig. 9a, Fig. 9b and Fig 10, correspondingly.

RQ2. Accuracy Evaluation

Accuracy results

Run

$ python3 ./reproducing_RQ2/gen_table.py

The generated results:

accuracy