Software Engineer
Writing Tests
What You Do Today
Write unit tests, integration tests, and sometimes end-to-end tests. Nobody loves writing tests, but everyone loves having them when something breaks. You're constantly balancing 'we should have better coverage' with 'we need to ship this feature by Friday.'
AI That Applies
AI test generation that analyzes your code and creates unit tests covering common paths, edge cases, and error conditions. LLM-powered test scaffolding that writes the boilerplate test structure so you can focus on the interesting assertions.
Technologies
How It Works
The system ingests code and creates unit tests covering common paths as its primary data source. A language model generates initial drafts by synthesizing the input context with learned patterns, producing text that follows the specified tone, format, and domain conventions. The output — unit tests covering common paths — surfaces in the existing workflow where the practitioner can review and act on it.
What Changes
Test coverage goes up without the grind. The AI generates the 15 boring test cases for input validation so you can focus on the 3 complex integration tests that actually require thought.
What Stays
Deciding WHAT to test and what the expected behavior should be. Writing tests is really about defining the contract — what should this code do? That's a design decision, not a typing exercise.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for writing tests, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long writing tests takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your engineering manager or VP Eng
“What's our current capability gap in writing tests — and is it a people problem, a tools problem, or a process problem?”
They're deciding which AI developer tools to adopt team-wide
your DevOps or platform team lead
“How would we know if AI actually improved writing tests — what would we measure before and after?”
They manage the infrastructure that AI tools depend on
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.