The scenario
Repair a checkout rule graph, evaluate prerequisites, and reconnect dependencies when rules are removed. This original practice follows a ByteDance community report that explicitly describes using a personal AI coding tool.
A failure to reason about
Removing the middle rule from a chain should preserve the surviving prerequisite. A rejected order can still conceal incorrect skipped-rule counts.
Illustrative context for the topic. The exercise’s full requirements arrive in the workspace.
Questions to bring to your review
- When is a rule ready to run?
- Must every prerequisite pass?
- Which edges survive a deletion?
- Does a reused ID inherit old state?
Skills you will practice
- DAG invariants
- Dependency evaluation
- Graph contraction
- Boundary testing
- AI code review
Read the existing code, reproduce the reported behavior, review proposed changes, and verify your final implementation. You can use AI assistance while staying responsible for the result.
Prepare with a worked example
- How to review AI-generated code in an interview
Work through a flawed AI patch, design a regression that exposes it, and explain a repair with evidence. A practical code-review exercise for AI-assisted interviews.
See how nrml evaluates an attempt, including the limits of automated evidence.