srlabusask / icse_nier_paper165_replication Goto Github PK

• copy the code

  git clone https://github.com/avijit1258/ICSE_NIER_paper165_replication.git

• go inside the downloaded code

  cd ICSE_NIER_paper165_replication

• create Python virtual environment

  python3 -m venv virtual_env

• activate the virtual environment

  source virtual_env/bin/activate

• install required packages

  pip install -r requirements.txt

• run the app.py

  python3 app/app.py

• If we want to generate ACS tree for a new subject system (for example, Detectron), we need to generate TGF file from the Python source code.

• The TGF file should go to the dataset folder.

• To generate TGF file from Python code, we need to install pyan3 (https://github.com/Technologicat/pyan).

  pyan3 /home/avijit/Detectron/detectron/**/*.py --uses --no-defines --colored --grouped --annotated -f subject_system.txt --uses --no-defines --tgf

• pyan3 might cause some error depending on the Python version of the subject system. Please consult their GitHub repository for issues.

• Update the variables in config.py file.

• Now, we need to run ClusteringCallGraph.py to generate the ACS tree at output directory.

  python3 app/ClusteringCallGraph.py

• To visualize the ACS tree, run the app.py now.

  python3 app/app.py

• visit https://hcpc.usask.ca to check out hosted application
• select subject system from left side of the panel
• click the Load ACS Tree button to load ACS tree of the selected subject system
• right click on the nodes to see their corresponding node summary, execution patterns and files related to that node
• select any function at Highlight nodes with similar function dropdown, the nodes in the ACS tree will be highlighted for guided browsing

The recommended Python version is 3.8.