feat: Disqualify exercises with >=4 parse errors

benchmark/problem_stats.py

@@ -28,6 +28,8 @@ def load_results(dirname):
         return None
 
     all_results = []
+    parse_errors = []  # Track which exercises had parse errors for this model
+    
     # Look in language subdirectories under exercises/practice
     for fname in benchmark_dir.glob("*/exercises/practice/*/.aider.results.json"):
         try:
@@ -36,13 +38,21 @@ def load_results(dirname):
             lang = fname.parts[-5]  # Get language from path
             results["language"] = lang
             all_results.append(results)
+            
         except json.JSONDecodeError:
+            # Track the parse error for this exercise/model combination
+            lang = fname.parts[-5]
+            exercise = f"{fname.parts[-2]}/{lang}"  # Use directory name as testcase
+            parse_errors.append(exercise)
             print(f"Failed to parse {fname}")
             continue
-    return all_results
+            
+    return all_results, parse_errors
 
 
 def analyze_exercise_solutions(dirs=None, topn=None, copy_hard_set=False):
+    PARSE_ERROR_M = 4  # Threshold for number of parse errors to DQ an exercise
+
     if dirs is None:
         # Use leaderboard data if no directories specified
         dir_entries = get_dirs_from_leaderboard()
@@ -52,9 +62,13 @@ def analyze_exercise_solutions(dirs=None, topn=None, copy_hard_set=False):
 
     # Filter out entries that don't load and sort by pass rate
     valid_entries = []
+    parse_errors_by_model = {}  # Track which exercises had parse errors for each model
+    
     for dirname, model in dir_entries:
-        results = load_results(dirname)
-        if results:
+        results_data = load_results(dirname)
+        if results_data:
+            results, model_parse_errors = results_data
+            parse_errors_by_model[model] = set(model_parse_errors)
             # Calculate pass rate for sorting when using custom dirs
             if dirs is not None:
                 pass_rate = sum(
@@ -181,10 +195,30 @@ def analyze_exercise_solutions(dirs=None, topn=None, copy_hard_set=False):
         cumsum += count
         print(f"{i:>6d}  {count:>9d}  {cumsum:>10d}")
 
+    # Count parse errors per exercise
+    parse_error_counts = defaultdict(int)
+    for model_errors in parse_errors_by_model.values():
+        for exercise in model_errors:
+            parse_error_counts[exercise] += 1
+
+    # Find exercises to disqualify based on parse error threshold
+    disqualified_exercises = {
+        exercise for exercise, count in parse_error_counts.items() 
+        if count >= PARSE_ERROR_M
+    }
+
+    if disqualified_exercises:
+        print(f"\nDisqualified {len(disqualified_exercises)} exercises with {PARSE_ERROR_M}+ parse errors:")
+        for ex in sorted(disqualified_exercises):
+            print(f"  {ex} ({parse_error_counts[ex]} parse errors)")
+
     # Collect the hard set (exercises solved by HARD_SET_NUM or fewer models)
     print(f"\nHard Set Analysis (exercises solved by ≤{HARD_SET_NUM} models):")
     print("-" * 60)
-    hard_set = {ex for ex, models in exercise_solutions.items() if len(models) <= HARD_SET_NUM}
+    hard_set = {
+        ex for ex, models in exercise_solutions.items() 
+        if len(models) <= HARD_SET_NUM and ex not in disqualified_exercises
+    }
     print(f"Total hard set exercises: {len(hard_set)}")
 
     # Count total problems, unsolved problems, and hard set problems by language