Skip to content

Latest commit

 

History

History
99 lines (65 loc) · 3.72 KB

README.md

File metadata and controls

99 lines (65 loc) · 3.72 KB
Raw

fabric8-analytics-rudra

This library is the collection of the common utils and tools required by various fabric8-analytics machine learning projects. Documentation is available at fabric8-analytics-rudra Docs. Please follow this article Docs Examples for documenting the code.

Installation:

$ pip install git+https://github.com/fabric8-analytics/fabric8-analytics-rudra

Local Build & Install:

$ make install

Testing:

$ make test

Documentation:

$ export PYTHONPATH=/path/to/fabric8-analytics-rudra
$ make html

Footnotes

Coding standards

  • You can use scripts run-linter.sh and check-docstyle.sh to check if the code follows PEP 8 and PEP 257 coding standards. These scripts can be run w/o any arguments:
./run-linter.sh
./check-docstyle.sh

The first script checks the indentation, line lengths, variable names, white space around operators etc. The second script checks all documentation strings - its presence and format. Please fix any warnings and errors reported by these scripts.

List of directories containing source code, that needs to be checked, are stored in a file directories.txt

Code complexity measurement

The scripts measure-cyclomatic-complexity.sh and measure-maintainability-index.sh are used to measure code complexity. These scripts can be run w/o any arguments:

./measure-cyclomatic-complexity.sh

and:

./measure-maintainability-index.sh

The first script measures cyclomatic complexity of all Python sources found in the repository. Please see this table for further explanation how to comprehend the results.

The second script measures maintainability index of all Python sources found in the repository. Please see the following link with explanation of this measurement.

You can specify command line option --fail-on-error if you need to check and use the exit code in your workflow. In this case the script returns 0 when no failures has been found and non zero value instead.

Dead code detection

The script detect-dead-code.sh can be used to detect dead code in the repository. This script can be run w/o any arguments:

./detect-dead-code.sh

Please note that due to Python's dynamic nature, static code analyzers are likely to miss some dead code. Also, code that is only called implicitly may be reported as unused.

Because of this potential problems, only code detected with more than 90% of confidence is reported.

List of directories containing source code, that needs to be checked, are stored in a file directories.txt

Common issues detection

The script detect-common-errors.sh can be used to detect common errors in the repository. This script can be run w/o any arguments:

./detect-common-errors.sh

Please note that only semantical problems are reported.

List of directories containing source code, that needs to be checked, are stored in a file directories.txt

Check for scripts written in BASH

The script named check-bashscripts.sh can be used to check all BASH scripts (in fact: all files with the .sh extension) for various possible issues, incompatibilities, and caveats. This script can be run w/o any arguments:

./check-bashscripts.sh

Please see the following link for further explanation, how the ShellCheck works and which issues can be detected.