Debug Common Sorting and Searching Problems in Data Structures and Algorithms

Start with the exact symptom

For Sorting and Searching, preserve the original symptom and capture the evidence expected from the failing boundary: the module-specific command, output, test, rendered state, query result, log, or measurement that proves the exercise worked. Diagnose it within this path context: Use invariants, input size, operations, asymptotic cost, memory tradeoffs, and worked data-structure states to evaluate the concept.

Keep the reproduction narrow and repeatable.

Reproduce the smallest failing case

For Sorting and Searching, start from this failure: Off-by-one boundaries. Diagnose and retest through this implementation lens: Use invariants, input size, operations, asymptotic cost, memory tradeoffs, and worked data-structure states to evaluate the concept.

Reduce the case until the important failure remains but unrelated application behavior is removed.

Follow the diagnostic evidence

Diagnose Sorting and Searching from the first useful signal. Start with this known failure pattern—Off-by-one boundaries.—and interpret it through this path context: Use invariants, input size, operations, asymptotic cost, memory tradeoffs, and worked data-structure states to evaluate the concept.

1. 1

   Off-by-one boundaries.

2. 2

   Incorrect base/termination condition.

3. 3

   Complexity hidden by nested work.

4. 4

   Algorithm assumes sorted or unique input when it is not.

import traceback

def reproduce():
    raise RuntimeError('Off-by-one boundaries.')

try:
    reproduce()
except Exception as exc:
    print('type:', type(exc).__name__)
    print('message:', exc)
    traceback.print_exc(limit=1)

python3 sorting-and-searching-diagnose.py

A stable exception type/message and traceback location; replace the controlled failure with the fix and rerun the same script.

practice/\n├── README.md\n├── sorting-and-searching-diagnose.py\n└── evidence/\n    └── expected-result.txt

Correct one cause

For Sorting and Searching, apply one correction that directly explains the observed evidence. Preserve unrelated conditions and retest using the same path-specific mechanism: Use invariants, input size, operations, asymptotic cost, memory tradeoffs, and worked data-structure states to evaluate the concept.

Prove recovery with the same check

Rerun the exact Sorting and Searching reproduction, then repeat the normal valid case. Record the module-specific command, output, test, rendered state, query result, log, or measurement that proves the exercise worked and interpret recovery through this path context: Use invariants, input size, operations, asymptotic cost, memory tradeoffs, and worked data-structure states to evaluate the concept.

Common mistakes to avoid

• Off-by-one boundaries.
• Incorrect base/termination condition.
• Complexity hidden by nested work.
• Algorithm assumes sorted or unique input when it is not.