Project: Debugging a Student Grading App in Python0%

Project: Debugging a Student Grading App in Python

Intermediate9 min read•Updated: 2026-10-01

Project: Debugging a Student Grading App

In this capstone project, we will apply the debugging and diagnostic techniques mastered throughout this chapter—Structured Logging, Logging Levels, Exception Traceback Capture, and Invariant Assertions—to diagnose and resolve logical and runtime bugs in an enterprise Student Grading & Analytics Application.

1. The Scenario & Buggy Application

Imagine inheriting a legacy grading script that crashes unexpectedly on certain student batches and produces incorrect honors classifications.

The Buggy Code (Before Debugging):

1# buggy_grader.py (Contains subtle bugs)
2students = [
3    {"name": "Aarav", "scores": [85, 92, 78]},
4    {"name": "Diya", "scores": ["95", 88, 91]},       # Bug A: Score is a string!
5    {"name": "Kavya", "scores": []},                   # Bug B: Empty list causes ZeroDivision!
6    {"name": "Rohan", "scores": [105, 80, 75]},        # Bug C: Score > 100 violates invariant!
7]
8 
9def calculate_grade(avg):
10    if avg >= 90:
11        return "A"
12    elif avg >= 80:
13        return "B"
14    elif avg >= 70:
15        return "C"
16    else:
17        return "F"
18 
19def grade_all(batch):
20    for s in batch:
21        total = sum(s["scores"])  # Crashes on Diya (TypeError: unsupported operand)
22        avg = total / len(s["scores"])  # Crashes on Kavya (ZeroDivisionError)
23        print(f"{s['name']}: Avg={avg}, Grade={calculate_grade(avg)}")

2. Implementing Diagnostic Logging Architecture

Instead of blindly adding print() statements, we establish a robust dual-channel logging architecture:

1
Console Handler: Emits INFO and higher messages for general application feedback.
2
File Handler (grading_audit.log): Emits fine-grained DEBUG logs containing exact numerical calculations, variable types, and captured tracebacks.

1import logging
2import sys
3 
4# Configure root logger
5logger = logging.getLogger("GradingEngine")
6logger.setLevel(logging.DEBUG)
7 
8# 1. Console Handler (INFO level)
9console_handler = logging.StreamHandler(sys.stdout)
10console_handler.setLevel(logging.INFO)
11console_formatter = logging.Formatter("%(levelname)-8s | %(message)s")
12console_handler.setFormatter(console_formatter)
13 
14# 2. File Handler (DEBUG level for full audit history)
15file_handler = logging.FileHandler("grading_audit.log", mode="w", encoding="utf-8")
16file_handler.setLevel(logging.DEBUG)
17file_formatter = logging.Formatter("%(asctime)s [%(levelname)s] (%(funcName)s) - %(message)s")
18file_handler.setFormatter(file_formatter)
19 
20# Attach handlers
21if not logger.handlers:
22    logger.addHandler(console_handler)
23    logger.addHandler(file_handler)

3. The Refactored, Bug-Free Grading Engine

Here is the fully instrumented, defensively programmed grading engine incorporating validations and assertions:

1from typing import List, Dict, Any, Optional, Tuple
2 
3 
4def sanitize_scores(raw_scores: List[Any], student_name: str) -> List[float]:
  """Validates, casts to float, and checks domain invariants (0 <= score <= 100)."""
  cleaned = []
  for idx, item in enumerate(raw_scores):
      try:
          score = float(item)
      except (ValueError, TypeError):
          logger.error(f"Student '{student_name}': Invalid non-numeric score '{item}' at index {idx}.")
          continue
13 
      # Invariant check: Scores must be in [0, 100]
      if score < 0 or score > 100:
          logger.warning(f"Student '{student_name}': Score {score} out of bounds (0-100). Clamping to range.")
          score = max(0.0, min(100.0, score))
18 
      cleaned.append(score)
20 
  logger.debug(f"Student '{student_name}': Raw count={len(raw_scores)}, Cleaned count={len(cleaned)}")
  return cleaned
23 
24 
25def compute_student_performance(student: Dict[str, Any]) -> Optional[Tuple[float, str]]:
  """Calculates sanitized average and letter grade for a single student."""
  name = student.get("name", "Unknown")
  raw_scores = student.get("scores", [])
29 
  logger.debug(f"Processing evaluation for student: {name}")
31 
  sanitized = sanitize_scores(raw_scores, name)
33 
  # Prevent ZeroDivisionError for students with no valid scores
  if not sanitized:
      logger.warning(f"Student '{name}' has no valid submitted scores. Marked as INCOMPLETE.")
      return None
38 
  total = sum(sanitized)
  avg = total / len(sanitized)
  
  # Internal developer assertion: Average must be within theoretical bounds
  assert 0.0 <= avg <= 100.0, f"Average {avg} violated mathematical bounds!"
44 
  if avg >= 90.0:
      grade = "A"
  elif avg >= 80.0:
      grade = "B"
  elif avg >= 70.0:
      grade = "C"
  elif avg >= 60.0:
      grade = "D"
  else:
      grade = "F"
55 
  logger.debug(f"Student '{name}': Total={total:.1f}, Avg={avg:.2f}, Grade={grade}")
  return avg, grade
58 
59 
60def process_grading_pipeline(students_data: List[Dict[str, Any]]):
  """Processes batch grading and prints summary dashboard."""
  logger.info(f"Starting grading batch for {len(students_data)} records...")
  
  successful_evaluations = 0
  
  print("\n" + "=" * 55)
  print(f"{'STUDENT':<15} | {'AVERAGE':<10} | {'GRADE':<8} | {'STATUS'}")
  print("-" * 55)
69 
  for student in students_data:
      name = student.get("name", "Unknown")
      try:
          result = compute_student_performance(student)
          if result:
              avg, grade = result
              print(f"{name:<15} | {avg:<10.2f} | {grade:<8} | PASS")
              successful_evaluations += 1
          else:
              print(f"{name:<15} | {'N/A':<10} | {'N/A':<8} | INCOMPLETE")
      except Exception as err:
          logger.exception(f"Unexpected fatal error evaluating student '{name}': {err}")
82 
  print("=" * 55)
  logger.info(f"Grading run completed: {successful_evaluations}/{len(students_data)} students graded successfully.")
85 
86 
87# Test Dataset with All Edge Cases
88student_batch = [
  {"name": "Aarav Sharma", "scores": [85, 92, 78]},
  {"name": "Diya Patel", "scores": ["95", 88, 91]},       # Strings handled safely
  {"name": "Kavya Nair", "scores": []},                   # Zero scores handled gracefully
  {"name": "Rohan Verma", "scores": [105, 80, 75]},       # Out of bounds score clamped to 100
  {"name": "Meera Sen", "scores": [70, "invalid", 80]},   # Corrupt score discarded with error log
94]
95 
96if __name__ == "__main__":
  process_grading_pipeline(student_batch)

4. Execution Output & Audit Verification

Console Output:

1INFO     | Starting grading batch for 5 records...
2WARNING  | Student 'Kavya Nair' has no valid submitted scores. Marked as INCOMPLETE.
3WARNING  | Student 'Rohan Verma': Score 105.0 out of bounds (0-100). Clamping to range.
4ERROR    | Student 'Meera Sen': Invalid non-numeric score 'invalid' at index 1.
5 
6=======================================================
7STUDENT         | AVERAGE    | GRADE    | STATUS
8-------------------------------------------------------
9Aarav Sharma    | 85.00      | B        | PASS
10Diya Patel      | 91.33      | A        | PASS
11Kavya Nair      | N/A        | N/A      | INCOMPLETE
12Rohan Verma     | 85.00      | B        | PASS
13Meera Sen       | 75.00      | C        | PASS
14=======================================================
15INFO     | Grading run completed: 4/5 students graded successfully.

Log File Output (`grading_audit.log`):

12026-09-12 15:55:00 [DEBUG] (compute_student_performance) - Processing evaluation for student: Aarav Sharma
22026-09-12 15:55:00 [DEBUG] (sanitize_scores) - Student 'Aarav Sharma': Raw count=3, Cleaned count=3
32026-09-12 15:55:00 [DEBUG] (compute_student_performance) - Student 'Aarav Sharma': Total=255.0, Avg=85.00, Grade=B
42026-09-12 15:55:00 [DEBUG] (compute_student_performance) - Processing evaluation for student: Diya Patel
52026-09-12 15:55:00 [DEBUG] (sanitize_scores) - Student 'Diya Patel': Raw count=3, Cleaned count=3
62026-09-12 15:55:00 [DEBUG] (compute_student_performance) - Student 'Diya Patel': Total=274.0, Avg=91.33, Grade=A
7...

⚠️ Common Intermediate Mistakes & Gotchas

1. Off-by-One Loop Indices

Iterating with range(len(items) - 1) skips the final element. Use idiomatic for item in items: or range(len(items)).

2. Integer Division Discarding Decimals

In algorithms calculating averages or percentages, ensure values are cast or divided with / (float division) rather than // (floor division).

💻 Try It Yourself: Extension Challenge - Zero Marks Handling

Challenge

Enhance the Student Grading App to catch students who were absent (marked as None or negative) and distinguish them from students who genuinely scored zero.

Complete Solution

1def sanitize_marks(marks_list):
2    valid_scores = []
3    for m in marks_list:
4        if m is None or m < 0:
5            print(f"Warning: Discarding invalid score: {m}")
6            continue
7        valid_scores.append(m)
8    return valid_scores
9 
10scores = [85, None, 92, -5, 0, 78]
11cleaned = sanitize_marks(scores)
12print(f"Cleaned Scores: {cleaned} -> Average: {sum(cleaned)/len(cleaned):.2f}")

Expected Output

1Warning: Discarding invalid score: None
2Warning: Discarding invalid score: -5
3Cleaned Scores: [85, 92, 0, 78] -> Average: 63.75

Interactive Quiz• Knowledge Check

Practice Quiz

Question 1

In our refactored grading application, why do we configure two separate handlers (console and file)?

Question 2

How did the refactored code handle string scores like "95" without crashing?

Question 3

What prevents the ZeroDivisionError when a student has submitted zero scores?

Question 4

What is the role of assert 0.0 <= avg <= 100.0 in compute_student_performance()?

Question 5

Why is logger.exception() preferred over logger.error() inside the pipeline's top-level catch-all block?

🚀 What's Next?

In the next lesson, we will continue your intermediate Python journey with Math and Random Module (10: Python Standard Libraries).

👉 Continue to Next Lesson: Math and Random Module →

Next Lesson

Math and Random Module

Continue learning with hands-on practice, examples, and exercises in the upcoming topic.

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Previous Lesson	Next Lesson
Assertions	Math and Random Module

Practice Quiz

Test your understanding of this lesson with 5 questions. Each question has one correct answer.

Previous Next

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Math and Random Module

Project: Debugging a Student Grading App in Python

Intermediate9 min read•Updated: 2026-10-01

Study Materials Cheatsheet

Project: Debugging a Student Grading App

1. The Scenario & Buggy Application

Imagine inheriting a legacy grading script that crashes unexpectedly on certain student batches and produces incorrect honors classifications.

The Buggy Code (Before Debugging):

1# buggy_grader.py (Contains subtle bugs)
2students = [
3    {"name": "Aarav", "scores": [85, 92, 78]},
4    {"name": "Diya", "scores": ["95", 88, 91]},       # Bug A: Score is a string!
5    {"name": "Kavya", "scores": []},                   # Bug B: Empty list causes ZeroDivision!
6    {"name": "Rohan", "scores": [105, 80, 75]},        # Bug C: Score > 100 violates invariant!
7]
8 
9def calculate_grade(avg):
10    if avg >= 90:
11        return "A"
12    elif avg >= 80:
13        return "B"
14    elif avg >= 70:
15        return "C"
16    else:
17        return "F"
18 
19def grade_all(batch):
20    for s in batch:
21        total = sum(s["scores"])  # Crashes on Diya (TypeError: unsupported operand)
22        avg = total / len(s["scores"])  # Crashes on Kavya (ZeroDivisionError)
23        print(f"{s['name']}: Avg={avg}, Grade={calculate_grade(avg)}")

2. Implementing Diagnostic Logging Architecture

Instead of blindly adding print() statements, we establish a robust dual-channel logging architecture:

1
Console Handler: Emits INFO and higher messages for general application feedback.
2
File Handler (grading_audit.log): Emits fine-grained DEBUG logs containing exact numerical calculations, variable types, and captured tracebacks.

1import logging
2import sys
3 
4# Configure root logger
5logger = logging.getLogger("GradingEngine")
6logger.setLevel(logging.DEBUG)
7 
8# 1. Console Handler (INFO level)
9console_handler = logging.StreamHandler(sys.stdout)
10console_handler.setLevel(logging.INFO)
11console_formatter = logging.Formatter("%(levelname)-8s | %(message)s")
12console_handler.setFormatter(console_formatter)
13 
14# 2. File Handler (DEBUG level for full audit history)
15file_handler = logging.FileHandler("grading_audit.log", mode="w", encoding="utf-8")
16file_handler.setLevel(logging.DEBUG)
17file_formatter = logging.Formatter("%(asctime)s [%(levelname)s] (%(funcName)s) - %(message)s")
18file_handler.setFormatter(file_formatter)
19 
20# Attach handlers
21if not logger.handlers:
22    logger.addHandler(console_handler)
23    logger.addHandler(file_handler)

3. The Refactored, Bug-Free Grading Engine

Here is the fully instrumented, defensively programmed grading engine incorporating validations and assertions:

1from typing import List, Dict, Any, Optional, Tuple
2 
3 
4def sanitize_scores(raw_scores: List[Any], student_name: str) -> List[float]:
  """Validates, casts to float, and checks domain invariants (0 <= score <= 100)."""
  cleaned = []
  for idx, item in enumerate(raw_scores):
      try:
          score = float(item)
      except (ValueError, TypeError):
          logger.error(f"Student '{student_name}': Invalid non-numeric score '{item}' at index {idx}.")
          continue
13 
      # Invariant check: Scores must be in [0, 100]
      if score < 0 or score > 100:
          logger.warning(f"Student '{student_name}': Score {score} out of bounds (0-100). Clamping to range.")
          score = max(0.0, min(100.0, score))
18 
      cleaned.append(score)
20 
  logger.debug(f"Student '{student_name}': Raw count={len(raw_scores)}, Cleaned count={len(cleaned)}")
  return cleaned
23 
24 
25def compute_student_performance(student: Dict[str, Any]) -> Optional[Tuple[float, str]]:
  """Calculates sanitized average and letter grade for a single student."""
  name = student.get("name", "Unknown")
  raw_scores = student.get("scores", [])
29 
  logger.debug(f"Processing evaluation for student: {name}")
31 
  sanitized = sanitize_scores(raw_scores, name)
33 
  # Prevent ZeroDivisionError for students with no valid scores
  if not sanitized:
      logger.warning(f"Student '{name}' has no valid submitted scores. Marked as INCOMPLETE.")
      return None
38 
  total = sum(sanitized)
  avg = total / len(sanitized)
  
  # Internal developer assertion: Average must be within theoretical bounds
  assert 0.0 <= avg <= 100.0, f"Average {avg} violated mathematical bounds!"
44 
  if avg >= 90.0:
      grade = "A"
  elif avg >= 80.0:
      grade = "B"
  elif avg >= 70.0:
      grade = "C"
  elif avg >= 60.0:
      grade = "D"
  else:
      grade = "F"
55 
  logger.debug(f"Student '{name}': Total={total:.1f}, Avg={avg:.2f}, Grade={grade}")
  return avg, grade
58 
59 
60def process_grading_pipeline(students_data: List[Dict[str, Any]]):
  """Processes batch grading and prints summary dashboard."""
  logger.info(f"Starting grading batch for {len(students_data)} records...")
  
  successful_evaluations = 0
  
  print("\n" + "=" * 55)
  print(f"{'STUDENT':<15} | {'AVERAGE':<10} | {'GRADE':<8} | {'STATUS'}")
  print("-" * 55)
69 
  for student in students_data:
      name = student.get("name", "Unknown")
      try:
          result = compute_student_performance(student)
          if result:
              avg, grade = result
              print(f"{name:<15} | {avg:<10.2f} | {grade:<8} | PASS")
              successful_evaluations += 1
          else:
              print(f"{name:<15} | {'N/A':<10} | {'N/A':<8} | INCOMPLETE")
      except Exception as err:
          logger.exception(f"Unexpected fatal error evaluating student '{name}': {err}")
82 
  print("=" * 55)
  logger.info(f"Grading run completed: {successful_evaluations}/{len(students_data)} students graded successfully.")
85 
86 
87# Test Dataset with All Edge Cases
88student_batch = [
  {"name": "Aarav Sharma", "scores": [85, 92, 78]},
  {"name": "Diya Patel", "scores": ["95", 88, 91]},       # Strings handled safely
  {"name": "Kavya Nair", "scores": []},                   # Zero scores handled gracefully
  {"name": "Rohan Verma", "scores": [105, 80, 75]},       # Out of bounds score clamped to 100
  {"name": "Meera Sen", "scores": [70, "invalid", 80]},   # Corrupt score discarded with error log
94]
95 
96if __name__ == "__main__":
  process_grading_pipeline(student_batch)

4. Execution Output & Audit Verification

Console Output:

1INFO     | Starting grading batch for 5 records...
2WARNING  | Student 'Kavya Nair' has no valid submitted scores. Marked as INCOMPLETE.
3WARNING  | Student 'Rohan Verma': Score 105.0 out of bounds (0-100). Clamping to range.
4ERROR    | Student 'Meera Sen': Invalid non-numeric score 'invalid' at index 1.
5 
6=======================================================
7STUDENT         | AVERAGE    | GRADE    | STATUS
8-------------------------------------------------------
9Aarav Sharma    | 85.00      | B        | PASS
10Diya Patel      | 91.33      | A        | PASS
11Kavya Nair      | N/A        | N/A      | INCOMPLETE
12Rohan Verma     | 85.00      | B        | PASS
13Meera Sen       | 75.00      | C        | PASS
14=======================================================
15INFO     | Grading run completed: 4/5 students graded successfully.

Log File Output (`grading_audit.log`):

12026-09-12 15:55:00 [DEBUG] (compute_student_performance) - Processing evaluation for student: Aarav Sharma
22026-09-12 15:55:00 [DEBUG] (sanitize_scores) - Student 'Aarav Sharma': Raw count=3, Cleaned count=3
32026-09-12 15:55:00 [DEBUG] (compute_student_performance) - Student 'Aarav Sharma': Total=255.0, Avg=85.00, Grade=B
42026-09-12 15:55:00 [DEBUG] (compute_student_performance) - Processing evaluation for student: Diya Patel
52026-09-12 15:55:00 [DEBUG] (sanitize_scores) - Student 'Diya Patel': Raw count=3, Cleaned count=3
62026-09-12 15:55:00 [DEBUG] (compute_student_performance) - Student 'Diya Patel': Total=274.0, Avg=91.33, Grade=A
7...

⚠️ Common Intermediate Mistakes & Gotchas

1. Off-by-One Loop Indices

Iterating with range(len(items) - 1) skips the final element. Use idiomatic for item in items: or range(len(items)).

2. Integer Division Discarding Decimals

In algorithms calculating averages or percentages, ensure values are cast or divided with / (float division) rather than // (floor division).

💻 Try It Yourself: Extension Challenge - Zero Marks Handling

Challenge

Enhance the Student Grading App to catch students who were absent (marked as None or negative) and distinguish them from students who genuinely scored zero.

Complete Solution

1def sanitize_marks(marks_list):
2    valid_scores = []
3    for m in marks_list:
4        if m is None or m < 0:
5            print(f"Warning: Discarding invalid score: {m}")
6            continue
7        valid_scores.append(m)
8    return valid_scores
9 
10scores = [85, None, 92, -5, 0, 78]
11cleaned = sanitize_marks(scores)
12print(f"Cleaned Scores: {cleaned} -> Average: {sum(cleaned)/len(cleaned):.2f}")

Expected Output

1Warning: Discarding invalid score: None
2Warning: Discarding invalid score: -5
3Cleaned Scores: [85, 92, 0, 78] -> Average: 63.75

Interactive Quiz• Knowledge Check

Practice Quiz

Question 1

In our refactored grading application, why do we configure two separate handlers (console and file)?

Question 2

How did the refactored code handle string scores like "95" without crashing?

Question 3

What prevents the ZeroDivisionError when a student has submitted zero scores?

Question 4

What is the role of assert 0.0 <= avg <= 100.0 in compute_student_performance()?

Question 5

Why is logger.exception() preferred over logger.error() inside the pipeline's top-level catch-all block?

🚀 What's Next?

In the next lesson, we will continue your intermediate Python journey with Math and Random Module (10: Python Standard Libraries).

👉 Continue to Next Lesson: Math and Random Module →

Next Lesson

Math and Random Module

Continue learning with hands-on practice, examples, and exercises in the upcoming topic.

Related Lessons

Previous Lesson	Next Lesson
Assertions	Math and Random Module

Practice Quiz

Test your understanding of this lesson with 5 questions. Each question has one correct answer.

Previous Next

Project: Debugging a Student Grading App

1. The Scenario & Buggy Application

The Buggy Code (Before Debugging):

2. Implementing Diagnostic Logging Architecture

3. The Refactored, Bug-Free Grading Engine

4. Execution Output & Audit Verification

Console Output:

Log File Output (grading_audit.log):

⚠️ Common Intermediate Mistakes & Gotchas

1. Off-by-One Loop Indices

2. Integer Division Discarding Decimals

💻 Try It Yourself: Extension Challenge - Zero Marks Handling

Challenge

Complete Solution

Expected Output

Practice Quiz

🚀 What's Next?

Math and Random Module

Related Lessons

Practice Quiz

Loading Page Content...

Project: Debugging a Student Grading App

1. The Scenario & Buggy Application

The Buggy Code (Before Debugging):

2. Implementing Diagnostic Logging Architecture

3. The Refactored, Bug-Free Grading Engine

4. Execution Output & Audit Verification

Console Output:

Log File Output (grading_audit.log):

⚠️ Common Intermediate Mistakes & Gotchas

1. Off-by-One Loop Indices

2. Integer Division Discarding Decimals

💻 Try It Yourself: Extension Challenge - Zero Marks Handling

Challenge

Complete Solution

Expected Output

Practice Quiz

🚀 What's Next?

Math and Random Module

Related Lessons

Practice Quiz

Log File Output (`grading_audit.log`):

Log File Output (`grading_audit.log`):