Enhance advisory grouping mechanism

[TG1999]

We have started grouping advisories on basis of content like summary, packages, patches, severities and weaknesses. Which is a good start. But we need to have more heuristics like relating advisories with aliases and checking how much close two advisory summaries are.

[Dhirenderchoudhary]

@TG1999 I would like to take this and work on it.
And solve it asap.

Proposed changes :

1. Add new entries to Models.py(ISSUE_TYPE_CHOICES).
2. Migration.
3. Enhance Pipeline.
4. Add Tests for the changes.

Can I take this issue and work on it ?

[TG1999]

@Dhirenderchoudhary What are your plans to relate advisories ? What heuristics you are planning ?

[Dhirenderchoudhary]

@TG1999 I'm planning to add two heuristics:

1. Alias-based relating : Group advisories from different datasources when they share the same alias. If multiple sources report on the same alias, we flag them as POTENTIALLY_RELATED_BY_ALIASES.
2. Summary similarity : Within those alias groups, compare summaries using difflib.SequenceMatcher with a 0.8 threshold. If two advisories from different sources have near identical summaries, we flag them as SIMILAR_SUMMARIES.

For implementation, I'll follow the existing
detect_conflicting_advisories pattern add the new issue types to ISSUE_TYPE_CHOICES, write the pipeline steps and helper functions, create a migration, and add tests.

And if you want to add or suggest something on this you can add on I'll follow that structure.