fix: resolve stale label corruption in _get_train_dataset

[dev-aditya-hub]

Summary

_get_train_dataset in IncrementalLearning had two issues that this PR addresses:

• Unbound variable on first miss — label was only assigned inside if len(index[0]) == 1, so if the very first hard example had no match in data_labels.x, Python raised UnboundLocalError buried inside a generic RuntimeError("pipeline runs failed") with no actionable message
• Silent label corruption on subsequent misses — if any hard example after the first had no match, label silently retained the value from the previous loop iteration, writing the wrong label for that image to the training file with no error, no log, and no indication anything was wrong; training then proceeded on corrupted supervision signal, producing invalid benchmark metrics
• O(n) numpy scan per hard example — np.where(data_labels.x == old) did a full array scan on every iteration instead of once

The developer had suppressed pylint's E0606 ("possibly-used-before-assignment") warning on this function rather than fixing the underlying logic.

Fix

Build a label_index dict (path → label) once from data_labels.x and data_labels.y. For each hard example, look up the label in O(1) via the dict. If a path is not found, raise a RuntimeError immediately with a message that names the missing path and the index file — giving the user an actionable diagnosis (path normalization mismatch, missing entry) instead of a silent wrong result or an opaque crash.

# Before
if len(index[0]) == 1:
    label = data_labels.y[index[0][0]]
file.write(f"{new} {label}\n")   # label could be stale or unbound

# After
label_index = {path: lbl for path, lbl in zip(data_labels.x, data_labels.y)}
if old not in label_index:
    raise RuntimeError(f"Hard example '{old}' has no matching label in '{data_label_file}'. "
                       f"This would write a stale or unbound label, corrupting training data.")
file.write(f"{new} {label_index[old]}\n")

Files Changed

• core/testcasecontroller/algorithm/paradigm/incremental_learning/incremental_learning.py — rewrite _get_train_dataset to use a dict lookup with explicit error on missing path; remove pylint: disable=E0606 suppression that was masking the bug

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[gemini-code-assist]

Code Review

This pull request fixes various typos in docstrings, logs, and function names across the codebase and adds a .codespellrc configuration. It also refactors the _get_train_dataset method in incremental_learning.py to use a dictionary for efficient label lookups and adds error handling for missing labels. Feedback was provided to improve the grammar and pluralization of a log message in simulation_system_admin.py.

[gemini-code-assist]

The log message contains a typo ('Congratulation' should be plural) and the phrasing 'build successful' is grammatically incomplete. Consider using 'build was successful' or 'built successfully'.

Suggested change

	"Congratulation! The simulation environment build successful!")
	"Congratulations! The simulation environment build was successful!")