Add codereview_env/graders/arch_grader.py

codereview_env/graders/arch_grader.py

@@ -0,0 +1,39 @@
+from typing import List
+from codereview_env.models import Scenario, ActionRecord, Category, ActionType, Verdict
+from codereview_env.graders.grader_utils import find_best_match
+
+def grade_architectural_review(scenario: Scenario, history: List[ActionRecord]) -> float:
+    """Grade architectural review: 0.6 * issue_score + 0.2 * verdict_score + 0.2 * quality_score."""
+    critical_issues = scenario.ground_truth_issues
+    flagged_actions = [r for r in history if r.category == Category.ARCHITECTURE]
+    
+    # 1. Issue Detection (0.6)
+    found_count = 0
+    already_matched = set()
+    quality_bonus = 0.0
+    
+    for action in flagged_actions:
+        match = find_best_match(action, critical_issues, already_matched)
+        if match:
+            found_count += 1
+            already_matched.add(match.id)
+            # Quality: + bonus if body is descriptive (> 80 chars)
+            if len(action.body) > 80:
+                quality_bonus += 0.05
+    
+    issue_score = (found_count / len(critical_issues)) if critical_issues else 1.0
+    
+    # 2. Verdict Correctness (0.2)
+    verdict_score = 0.0
+    final_action = history[-1] if history else None
+    if final_action and final_action.action_type in (ActionType.APPROVE, ActionType.REQUEST_CHANGES):
+        # Check against the first required verdict in GT
+        required_verdicts = [gt.required_verdict for gt in critical_issues if gt.required_verdict]
+        if required_verdicts and final_action.verdict == required_verdicts[0]:
+            verdict_score = 1.0
+        elif not required_verdicts and final_action.verdict == Verdict.LGTM:
+            verdict_score = 1.0
+
+    # 3. Final weighted calculation
+    score = 0.6 * issue_score + 0.2 * verdict_score + min(0.2, quality_bonus)
+    return round(min(1.0, score), 4)