Message-ID: <gh-pgjdbc-pgjdbc-3965@github.com>
From: "vlsi (@vlsi)" <noreply+vlsi@github.com>
To: "pgjdbc/pgjdbc" <noreply+pgjdbc-pgjdbc@github.com>
Date: Mon, 16 Mar 2026 07:44:24 +0000
Subject: [pgjdbc/pgjdbc] PR #3965: chore: use greedy pairwise coverage for CI matrix generation
List-Id: <gh-pgjdbc-pgjdbc.github.com>
X-GitHub-Author-Id: 213894
X-GitHub-Author-Login: vlsi
X-GitHub-Issue: 3965
X-GitHub-Labels: chore
X-GitHub-Repo: pgjdbc/pgjdbc
X-GitHub-State: merged
X-GitHub-Type: pull_request
X-GitHub-Url: https://github.com/pgjdbc/pgjdbc/pull/3965
Content-Type: text/plain; charset=utf-8

Replace the random-per-axis row generation with a greedy algorithm that maximizes pairwise parameter coverage. For each row, 1000 random candidates are generated and the one covering the most previously-uncovered parameter pairs is selected.

Pair scoring uses normalized weights (weight_i × weight_j) so that production-like values (e.g. autosave=never, query_mode=extended) are prioritized while non-default values still appear for coverage. Candidate generation uses uniform random to ensure diverse candidates.

This achieves ~43-47% pair coverage and ~76% weighted coverage with 5 jobs.

Use `node matrix.mjs --coverage` to see pair coverage statistics.