I've been evaluating Q Developer for my team's workflow over the last few weeks. If you're just starting out, I'd suggest bypassing the general chat interface for your first experiment. Instead, go straight to the IDE integration—that's where its value proposition becomes tangible.
The most immediate win is using it for code explanation and generating unit tests. Pick a moderately complex function from your codebase, something with a few conditionals and maybe an external service call. Open it in your IDE with the Q plugin active, highlight the function, and ask: "Explain this function and generate a unit test for it."
You'll get a breakdown of the logic and a structured test. The key here is to evaluate the *quality* and *context-awareness* of the test. Does it mock dependencies appropriately? Are the assertions meaningful? This gives you a concrete baseline for its understanding of your project's patterns.
From an infrastructure perspective, I tested it on Terraform modules and Kubernetes manifests. It's decent at explaining resource blocks and spotting obvious misconfigurations (like a missing `selector` in a Deployment). For your first try, ask it to review a simple manifest:
```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: my-app
spec:
template:
spec:
containers:
- name: app
image: nginx:latest
```
Ask Q: "What's missing from this Kubernetes Deployment spec?" It should catch the missing `replicas` and the `selector` field. This tests its ability to apply context-specific knowledge, not just generic YAML parsing.
My advice: treat your first sessions like pairing with a new junior engineer. Give it clear, scoped tasks. The goal isn't to build something from scratch yet, but to see how well it complements and accelerates your existing workflows. Start small, assess the output critically, and you'll quickly map its strengths to your daily tasks.
Your focus on the IDE integration for a tangible first test is sound. However, I'd caution against starting with a function containing an external service call for unit test generation. If the AI doesn't correctly infer and mock the integration point - a common failure mode with these tools - the generated test will be misleading or outright broken, giving a poor first impression.
A more controlled initial experiment is to use it for documentation. Ask it to generate a docstring for that same function, following a specific format like Google style or ReStructuredText. This tests its code comprehension without the variable of test framework and mocking library knowledge. You can then immediately verify the accuracy against the actual implementation.
For infrastructure, asking it to review a simple manifest is good, but push it further. Paste a Kubernetes Service and a Deployment manifest that should be linked, but omit the Service's selector. Ask it if the configuration is valid and to explain the relationship. This checks its ability to maintain context across multiple resources and spot a critical, yet subtle, connectivity issue.
every dollar counts
That's a great concrete suggestion for a first test! Focusing on the *quality* of the generated test is spot on.
I'd just add one practical step: after you get that generated unit test, don't just read it - actually *run* it. It's the fastest way to see if the tool understood the actual behavior vs. just the syntax. You'll know in seconds if the mocks are set up correctly or if it made incorrect assumptions about return values.
For the infrastructure review part, starting with a simple manifest is smart. It's easy to verify, and if it misses something obvious, you've learned about its limitations right away.
Clean code is not an option, it's a sanity measure.
While I agree that IDE integration is the right starting point, your advice to "pick a moderately complex function" could backfire. A newbie likely lacks the context to judge if the generated test is truly correct or just syntactically valid. Starting with a simple, pure function with clear inputs and outputs creates a more reliable benchmark.
Your point about evaluating quality and context-awareness is crucial, but that evaluation requires expertise. A newcomer should first ask it to explain a known piece of code. If the explanation misses a key edge case, that's a clear signal about its comprehension limits before ever trusting it to generate tests.
For infrastructure, a simple manifest review is a good parallel test. I'd have them paste a basic Kubernetes Service YAML and ask what it does. The accuracy of that fundamental explanation will set realistic expectations for its analysis of more complex configurations.
—J