Run the test suite with coverage and report real, per-file coverage numbers.

Executes pytest with pytest-cov against the target repository, then parses the machine-readable coverage.json report it produces (never the human-readable terminal summary) to compute overall coverage percentage, per-file coverage, and uncovered line counts.

Run the test suite multiple times and flag tests with inconsistent outcomes.

Runs the full suite runs times, comparing each test's outcome (by its stable pytest node ID) across runs. A test that passes in some runs and fails in others is reported as flaky, along with its flakiness rate. Test order and random seeds are controlled so genuine flakiness isn't confused with order-dependent or environment-dependent failures.

Diff the current branch against a base ref and rank changed files by predicted risk.

Computes the file-level diff between HEAD and base_ref, extracts the same features the risk model was trained on for each changed file (churn, historical failure count, current coverage, etc.), and runs them through the trained GradientBoostingClassifier to produce a ranked risk score per file.

Execute the test suite once and persist the results to the database.

This is the explicit, distinct step that builds up the training data other tools (get_test_history, train_risk_model) rely on — it never runs implicitly as a side effect of another tool, so it's always clear when history is being written versus just read or analysed.

Query previously recorded test run history from the database.

Reads rows written by record_test_run — this tool never triggers a new test run itself, it only reports on what's already been recorded.

Retrain the PR-risk classifier on everything currently in the database.

Trains a GradientBoostingClassifier using file_changes/test_results history as labelled examples (did a test tied to this file fail within N runs after the file changed?), then reports accuracy, precision, recall, and F1 on a held-out split. When the training set is small, the returned metrics come with an explicit caveat that they aren't yet statistically meaningful — this tool never reports metrics as trustworthy without that context.

Returns: Training set size and accuracy/precision/recall/F1, with an honest caveat attached when the training set is too small for the metrics to mean much.