Software quality study scripts

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For a complete study about a monolithic software product being dismissed, please check STUDY.md

Preliminary information required

Annotate your code

Please read Smart commits.

Code coverage report

Please read this article.

The cyclomatic complexity report

Python

Please read this documentation.

radon cc src --json --output-file radon.json -i libs -e text_*.py

Procedure

setup the analysis parameters

configure the secrets.yaml

extract information from the software defects database

The script 1-extract-issues-from-jira.py allows the caller to connect to the software defects database and extract all the issues. Two functions (that you can customize) allow you to personalize it. The aim is to produce a CSV on three columns

Key,IsSupport,Priority
PRJ-8623,True,P2

extract issues from the code commits

Given that your commits have been decorated with the issue they are addressing, like

fix(PRJ-8623): some bug in production

issue PRJ-8623 blablabla

The script 2.extract-issues-from-sources.py extracts the issue Key and the impacted files. The aim is to produce a json file containing the list of issues for every file

{
    "app/server/my_model.py": [
        "PRJ-8334", "PRJ-5693", "PRJ-4709", "PRJ-2427", "PRJ-5034", "PRJ-5034", "PRJ-4937",
        "PRJ-3728", "PRJ-3712"
    ]
}

Only the issues mentioned by the code will be mentioned in the file.

aggregate code quality and issues

The aim of the script 3-aggregate-coverage-and-issues.py is to produce a report like

FILE,OWNER,ISSUES,BUGS,SUPPORT_BUGS,P1,P2,P3,P4,COMPLEXITY,HAL_VOLUME,COVERAGE,LINES,LINES_COVERED
"app/server/my_model.py",cms,8,5,4,1,3,1,0,10,450.3799746402741,0.3553,197,70

From the different sources of information about the code (coverage, radon-cc, radon-hal, software defects database export).

simple statistics

Now, let's try to output some statistics about the csv file aggregating all the file-level code quality indicators.