OCR Project: Iterative Development and Testing

Develop a manageable portion of the core behaviour, test it and review what the result means before building more dependent features. A prototype can investigate an uncertain algorithm, data model or user interaction. Its purpose should be explicit; a large unfinished program is not automatically a useful prototype.

Connect each important increment to the breakdown from analysis and design. Record what was implemented, why that order or technique was chosen and what evidence led to a revision. OCR expects principles from an agile approach, with evidence appearing across the development record; the report need not invent a perfect one-pass waterfall history. Meaningful iteration is a response to evidence.

Use modular responsibilities, appropriate identifiers and validation at important boundaries. Comments and annotations should explain non-obvious assumptions, invariants and design decisions. Explaining every assignment in a screenshot can obscure the important reasoning. A reviewer should be able to locate the core methods and understand how they serve the requirements.

Choose representative prototype versions and code references that show significant changes. Preserve enough context to understand what changed and why, while avoiding a pile of unexplained near-identical screenshots. Source/version history can help maintain genuine development records, but a commit list alone does not establish the quality or rationale of each implementation decision.

For each important development test, state its purpose, input, expected outcome and actual outcome with suitable evidence. If a boundary fails, explain the cause, the remedial action and why that action is appropriate. Then retest both the original failing case and relevant neighbouring or dependent behaviour. Do not replace the original evidence with an unsupported claim that everything worked first time.

The 25 development marks split into 15 for iterative development and 10 for testing that informs development. The split is not an automatic point for every screenshot: the quality of annotated evidence and justification matters. Keep actual results distinct from predictions. Never fabricate tests or feedback to create a complete-looking record; genuine limitations and reasoned remedies provide a more accurate account of the achieved work.

Attempt the task before opening its worked response. Record each change of state, explain the rule used, and check the result against the original requirements. Code examples labelled Python 3 use that language’s conventions; pseudocode states its own assumptions.