Pylint alerts corrections as part of an intervention experiment

[evidencebp]

Pylint alerts are correlated with tendency to bugs and harder maintenance.
I'd like to conduct a software engineering experiment regarding the benefit of Pylint alerts removal.
The experiment is described here.

In the experiments, Pylint is used with some specific alerts, files are selected for intervention and control.
After the interventions are done, one can wait and examine the results.

Your repository is expected to benefit from the interventions.
I'm asking for your approval for conducting an intervention in your repository.

See examples of interventions in stanford-oval/storm, gabfl/vault, and coreruleset/coreruleset.

You can see the planed interventions
The plan is to do 34 interventions in 31 files
The interventions will be of the following types:
line-too-long: 31
unnecessary-pass: 1
wildcard-import: 2

May I do the interventions, @evanepio?

[evanepio]

Sorry for the delay in response.

I don't think my project would be a good fit for your study. This project hasn't changed in years. Sure I've updated dependencies, but I have not enhanced or added features to it. I won't be able to give you an answer as to whether or not the linting helped. The existing bugs are unlikely to be discovered, and no new bugs are likely to get introduced when all I do is update dependencies.

A second reason why is that I already have my code linted using the ruff tool. And the files that would be affected by any intervention are mostly set to be ignored (generated database migration files). My line length is explicitly set to allow 120 characters (line 90 of pyproject.toml does this) so it's incompatible with the pylint settings your study has chosen. Pylint also missed my third wild-card import, so the settings might be off.

My apologies, and best of luck with your study. It sounds really cool and other projects will be able to benefit!

[evidencebp]

You are right, @evanepio.
I indeed need a project that is still in development in order to evaluate the impact on behavior.

Thank you for your time and attention.