Repeatability
High
CSV validation libraries follow a well-established structural pattern: parse rows, check types, validate enums, enforce required fields, collect errors with location metadata, and serialize results. This is highly repeatable with no novel judgment required per instance.
Ambiguity Tolerance
Medium
Core requirements are crisp — row/column error reporting, REST endpoint, JSON output — but the cross-field consistency rules are underspecified and would need concrete examples or a schema definition file to implement correctly. Success is largely verifiable but depends on receiving the actual schema spec.
Data & Tool Availability
High
A code agent needs only the schema definition (field names, types, enums, cross-field rules) and the target Spring Boot project structure, both of which are straightforward to provide. No external APIs, credentials, or live systems are required to generate the code.
Error Cost
Low
Generated code is fully reviewable and testable before deployment; no irreversible action is taken. A developer reviews, runs unit tests, and catches any logic errors before the library touches production data.
Human Judgment Required
Low
Library architecture, error message formatting, and Spring Boot wiring are all standard engineering decisions with well-known best practices. The only human input needed is the schema specification itself, which is a requirements artifact, not a judgment call during execution.