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We often hear that getting better results from AI is a matter of writing better prompts.

Be more specific. Give the AI a role. Add context. Explain what you want.

All of that can help. But: a prompt can be detailed, well-written, and still lead you in the wrong direction.

1. Don’t ask AI to confirm your decision. Ask it to evaluate it.

❌ Treating the decision as already made

“We’ve decided to move to microservices. Outline how to split our application into services.”

What’s wrong?

The decision is presented as settled, so the AI works on the next step. It will produce a sensible-looking list of services. It won’t usually stop to ask whether you need microservices at all.

Ask a model directly “Are microservices better than a monolith?” and it will usually say “it depends”. The risk is the prompt where the decision is built in and never questioned.

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AI writes code fast. Many teams then ask AI to write the tests for that code too.

That’s the problem. The tests check the code against itself. They don’t check it against what the business asked for.

It’s like a student marking their own exam. The answers always match, so they always pass. A test that can’t fail tells you nothing.

❌ Write test after code

The test comes from the code, not from the spec.

1. AI Reads Spec

AI reads the spec:

A customer gets the 10% promotion and their 20% coupon.

There are (at least) two ways to interpret the spec:

• Interpretation A: 10% off first, then 20% off what is left.

• Interpretation B: 10% + 20% = 30% off.

2. AI Writes Code

AI picks interpretation A: 10% off first, then 20% off the reduced price.

var promotedPrice = basePrice.multiply(promotionFactor);
var discountAmount = promotedPrice.multiply(discountRate);
var totalPrice = promotedPrice.subtract(discountAmount);

Note: AI is non-deterministic, so we can’t know which interpretation it will choose. In one run it may only think of interpretation A. In another run it may only think of B. In another it may see both and ask us to choose. And in another it may come up with an interpretation C that we hadn’t even thought of.

3. AI Writes Acceptance Test

AI reads its own code, then writes a test that mirrors the behavior of that code. It makes up inputs and answers that fit the code:

@TestTemplate
@Channel({ChannelType.UI, ChannelType.API})
void shouldApplyCouponOnPromotedPrice() {
    scenario
            .given().product().withUnitPrice(”100.00”)
                .and().promotion().isActive().withDiscount(”10%”)
                .and().coupon().withDiscountRate(”20%”)
            .when().placeOrder()
            .then().shouldSucceed()
                .and().order().hasTotalPrice(72.00);
}

4. Verify Acceptance Test Passes

The acceptance test is executed. It passes, because it was made to match the code, so the build is green.

5. Human discovers misinterpretation

The build is green, so the work goes to QA. QA reads the spec their own way. They expect $70.00 and see $72.00, so they report a bug. The bug goes to the product owner.

Only now does anyone ask the question that should have been asked first. The product owner says 30% off.

6. AI reworks code & test

The code is wrong, and the test is wrong too, because it was made to match the code. Both the code and the test must be reworked.

It was circular: the code decided the test, and then the test “proved” the code. That makes it tautological: true by construction, so it can never fail.

✅ Write test before code

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Markdown spec = enforced spec? WRONG.

Markdown = Spec. Acceptance Test = Executable Spec.

❌ Spec -> AI writes code -> Human manually tests

✅ Executable Spec -> AI writes code -> Automatic spec verification

99% of developers follow Spec-Driven Development (SDD) the WRONG way.

1. They write the spec in Markdown.
2. They tell AI to implement code based on spec.

But what's the problem here? Sure, AI reads the markdown file, but doesn't follow it fully! It may decide to skip instructions, or misinterpret instructions, etc.

So we can NOT trust AI!

We have to manually read the spec, manually run the system, and manually decide whether the functionality that the system implemented matches what was in the spec.

Interestingly, this is not a new problem. We've had this problem for ages, with human developers. Remember, we'd write a User Story, the developer thinks they followed it, but then QA says it doesn't work correctly - that there was something that the developer missed, or they had misinterpretations.

Well, that's exactly the same problem in Spec-Driven Development, how most people practice it.

The solution is: EXECUTABLE specs. With executable specs, we can be sure to enforce that AI actually implements the behavior that we specified. We have an automated way to check - a pass or fail. This means if AI skips instructions, or misinterprets something, we'll have a test telling us it failed. AI tries again, until the test passes. There's no way for AI to "cheat" the test.

That's the right way to do Spec-Driven Development. Convert textual specs to executable specs.

⚡Full walkthrough, with the code examples

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The Strategy pattern is usually introduced with a diagram:

Context
  └── Strategy
        ├── Strategy A
        ├── Strategy B
        └── Strategy C

Useful. But in a real application, the more important question is:

Where should those strategies live in Clean Architecture?

The problem

You’re placing an order, and the shipping cost depends on where it’s going.

public Money calculateShipping(Order order) {
    if (order.country().zone() == ShippingZone.DOMESTIC) {
        return Money.of(5);
    }

    return Money.of(15);
}

That works. Then the rules grow:

public Money calculateShipping(Order order) {
    var zone = order.country().zone();

    if (zone == ShippingZone.DOMESTIC) {
        return order.subtotal().isAtLeast(Money.of(50))
            ? Money.ZERO
            : Money.of(5);
    } else if (zone == ShippingZone.NORTH_AMERICA) {
        return Money.of(10).add(Money.of(1).multiply(order.itemCount()));
    } else if (zone == ShippingZone.INTERNATIONAL) {
        return Money.of(20)
            .add(Money.of(2).multiply(order.itemCount()))
            .add(Money.of(15));
    }

    throw new IllegalStateException(”Unknown zone: “ + zone);
}

Now the code that places the order knows every shipping rule in the system. Every new market means editing it. Every rate change means editing it.

Step 1: The domain defines the interface

Define what the business needs, not how it’s done:

public interface ShippingPolicy {
    Money calculate(Money subtotal, Quantity quantity);
}

This is something the business needs: “give me the shipping cost for an order of this value and size”. So the domain owns it.

Notice the name: ShippingPolicy, not ShippingStrategy. “Strategy” is the pattern’s name; “policy” is the business’s word for it. The interface is a Strategy, but you name it in the language of the domain.

Step 2: Each rule becomes a class

public final class DomesticShipping implements ShippingPolicy {

    private static final Money FREE_SHIPPING_THRESHOLD = Money.of(50);
    private static final Money FLAT_RATE = Money.of(5);

    @Override
    public Money calculate(Money subtotal, Quantity quantity) {
        if (subtotal.isAtLeast(FREE_SHIPPING_THRESHOLD)) {
            return Money.ZERO;
        }

        return FLAT_RATE;
    }
}

public final class NorthAmericaShipping implements ShippingPolicy {

    private static final Money BASE = Money.of(10);
    private static final Money PER_ITEM = Money.of(1);

    @Override
    public Money calculate(Money subtotal, Quantity quantity) {
        return BASE.add(PER_ITEM.multiply(quantity.value()));
    }
}

public final class InternationalShipping implements ShippingPolicy {

    private static final Money BASE = Money.of(20);
    private static final Money PER_ITEM = Money.of(2);
    private static final Money CUSTOMS_HANDLING = Money.of(15);

    @Override
    public Money calculate(Money subtotal, Quantity quantity) {
        return BASE
            .add(PER_ITEM.multiply(quantity.value()))
            .add(CUSTOMS_HANDLING);
    }
}

Each rule can change without touching the others or the use case.

Step 3: The domain selects the policy by shipping zone

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Mike is a software developer.

He had been asking for better requirements for years.

Not because he loved writing documentation — because he was tired of building the wrong thing.

The product owner would create a one-line ticket. He was always busy and also hated writing)

Then, in backlog refinement:

“Everyone understands?”

Nod. Nod.

Next ticket.

And then off the developers go to implement it.

Mike gets the blame

QA started reporting so many bugs, so many scenarios that developers didn’t think of.

The product owner saw the feature and shook his head:

“This isn’t what we wanted. You’ve basically implemented the wrong thing”

Mike was frustrated. This kept happening.

He brought it up to management.

The answer?

“We can't spend half the sprint on requirements.”

“Just start coding.”

“You’re the developer. You should figure it out.”

Then AI arrived

Suddenly, specifications were a priority.

Now management was pushing Spec-Driven Development down on the whole team.

Mike’s reaction?

“Wait. NOW specifications are important?”

But there was another problem.

AI would skip parts of the requirements or misinterpret them.

Unlike a developer would at least try to follow the specification, not skip stuff.

AI gives Mike more work

Mike has to spend a lot of time like reviewing AI’s work, and management is now angry at him. They’re paying for all these AI tools, but delivery is still not fast enough.

He’s really frustrated, because now he has to do a lot of checking: did AI actually satisfy the specifications or not?

And he has to do it all manually, like manually testing things before handing it over to QA — and it’s just boring, tedious.

Mike takes back control

Then Mike discovered that if you do Spec-Driven Development with actually executable specifications, that problem pretty much disappears.

So instead of just giving a user story to AI, he’d have AI turn it into acceptance tests first. He’d confirm the acceptance tests before AI started coding.

And all of a sudden, he was back in control.

AI couldn’t just do whatever it wanted.

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You’ve split your application into one class per use case.

Then you gave every use case the same interface — the Command Handler pattern:

public interface UseCase<TRequest, TResponse> {
    Result<TResponse, UseCaseError> execute(TRequest request);
}

One request in. One result out.

But you run into issues:

❗Problem #1: Cross-cutting concerns duplicated across use cases

Logging, transactions, authorization — many use cases need them, so you end up copying them into each one:

public final class PlaceOrder
        implements UseCase<PlaceOrderRequest, PlaceOrderResponse> {

    public Result<PlaceOrderResponse, UseCaseError> execute(
        PlaceOrderRequest request
    ) {
        log.info("PlaceOrder started");
        authorize(Permission.PLACE_ORDER);
        return transactional(() -> {
            // ... the actual use case logic
        });
    }
}

public final class CancelOrder
        implements UseCase<CancelOrderRequest, CancelOrderResponse> {

    public Result<CancelOrderResponse, UseCaseError> execute(
        CancelOrderRequest request
    ) {
        log.info("CancelOrder started");
        authorize(Permission.CANCEL_ORDER);
        return transactional(() -> {
            // ... the actual use case logic
        });
    }
}

The same lines repeat in many use cases, and every new use case has to remember them.

❗Problem #2 (secondary): Constructors with lots of injection

Every use case is its own class, so every controller needs each one it calls injected:

public OrderController(
    PlaceOrder placeOrder,
    ViewOrderDetails viewOrderDetails,
    CancelOrder cancelOrder,
    PublishCoupon publishCoupon,
    BrowseCoupons browseCoupons,
    // ... and more with every new use case
) { ... }

And each endpoint calls its own use case directly:

@PostMapping("/orders")
public ResponseEntity<?> placeOrder(@RequestBody PlaceOrderRequest request) {
    return mapResult(placeOrder.execute(request));
}

The constructor grows with every use case you add. The wiring becomes noise, and every controller test has to supply them all.

❌ Poor Solution: Base Classes

The tempting fix for Problem #1 is a BaseUseCase that every use case extends. But as it grows with logging, authorization, transactions, and error handling, every use case inherits all of it — whether it needs it or not.

And it does nothing for Problem #2: the controller still needs every use case injected.

✅ Effective Solution: Command Bus — in Practice

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Your team automated testing.

So why does every release still wait for QA?

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You invested in E2E test automation to get faster releases and fewer bugs.

Instead, what you got is a test suite that breaks with every UI change, a team that doesn’t trust it, and a manual regression pass that still sits between “done” and “released.”

Now AI is helping your developers write code faster, and the queue in front of QA is only getting longer.

This isn’t a people problem, and more automation of the same kind won’t fix it. It comes down to how the tests are designed.

What You’ll Learn

• Why E2E automation rarely replaces manual QA — and the three design decisions behind it

• What changes when tests are written in business language — your product owner can read them, QA can trust them, and UI changes stop breaking them

• How to start small — with one critical use case, without stopping delivery or rewriting everything

• How to get it funded — by presenting it to your CEO in terms of release speed, cost of bugs and time spent on repeat work

Who Should Join

This is for you if:

• You lead an engineering team (as engineering manager, head of engineering or CTO)

• Releases are slowed down by manual testing

• Your team tried test automation and it didn’t deliver what was promised

Event Details

📅 Date & Time: Wed, Oct 7, 2026 5:30PM
📍 Where: Live online
💰 Cost: Free

🔗 Register here: Why Your E2E Tests Didn’t Replace Manual QA

Meet Your Coach

Valentina Jemuović is a technical coach and the founder of Optivem. She coaches engineering teams in ATDD and TDD, helping them deliver software faster with fewer bugs. She works in Java, .NET and TypeScript, and shares her work with ~30K followers on LinkedIn.

Coding Got Faster — Releases Are Still Slow

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“Spec-driven development” is everywhere in AI tooling right now: Kiro, GitHub’s spec-kit, Tessl. Write the spec first, then let the agent build against it. Birgitta Böckeler reviewed all three in Understanding Spec-Driven-Development: Kiro, spec-kit, and Tessl on martinfowler.com and found the catch.

In every one of them, “spec” means a Markdown document the agent is supposed to read. Nothing checks that the code matches it, and the agents don’t always follow it. As she put it: “I frequently saw the agent ultimately not follow all the instructions.”

The problem isn’t the tools. It’s that the spec is a Markdown file, and you can’t run a Markdown file against the code. To find out whether the agent followed it, a person has to read the code line by line. That’s the work you hoped the spec would save.

We’ve had a spec without this problem since 1999. Kent Beck’s first Extreme Programming book, Extreme Programming Explained (1999), already had acceptance tests, and the XP rules on extremeprogramming.org spell out how they work: acceptance tests “are created from user stories” and “should be automated so they can be run often.”

In 2010, Jez Humble and Dave Farley’s Continuous Delivery gave automated acceptance tests their own chapter: “Acceptance tests are a crucial stage in the deployment pipeline: They take delivery teams beyond basic continuous integration. Once you have automated acceptance tests in place, you are testing the business acceptance criteria of your application, that is, validating that it provides users with valuable functionality.”

An acceptance test is the spec and the check in one: it describes what the system should do, and it fails until the system does it.

An agent can ignore a Markdown file.

It can’t ignore a test that won’t pass.

An acceptance test doesn’t call the system directly. It calls a DSL, and the DSL calls two kinds of drivers. System drivers talk to the system through its UI or API. External-system drivers talk to the external systems it depends on, such as an ERP.

In acceptance test-driven development (ATDD), the Red step is where you write that test, before any system code exists. When an AI agent does the work, the Red step is split into four small steps, and a human reviews each one.

In every box, the AI writes the code, a human reviews it, and then it’s committed.

Before Red: the acceptance criteria

Example: placing an order in an online shop.

It starts with a ticket. Its acceptance criteria are agreed before any code is written:

Scenario: Calculate the base price from unit price and quantity
  Given a product with unit price 20.00
  When I place an order for 5 of that product
  Then the order is placed successfully
  And the order’s base price is 100.00

Inside the Red step

1. Write Acceptance Tests

The agent turns the acceptance criteria into a test. The test is written in a DSL, the test code’s own vocabulary (placeOrder().withQuantity(...)). Here’s the acceptance test, from PlaceOrderPositiveTest.java:

@TestTemplate
@Channel({ChannelType.UI, ChannelType.API})
void shouldCalculateBasePriceAsProductOfUnitPriceAndQuantity() {
    scenario
            .given().product()
                .withUnitPrice(20.00)
            .when().placeOrder()
                .withQuantity(5)
            .then().shouldSucceed()
            .and().order()
                .hasBasePrice(100.00);
}

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Your SubscriptionService has 800 lines.

It activates subscriptions, handles cancellations, validates plan changes, checks expiration...

Meanwhile Subscription is just getters and setters.

That’s the problem.

❌Your Domain Is Just Data (Anemic Domain)

public class Subscription {
    private String customerId;
    private SubscriptionPlan plan;
    private SubscriptionStatus status;
    private LocalDate startedAt;
    private LocalDate expiresAt;
    private BigDecimal price;

    public String getCustomerId() {
        return customerId;
    }

    public void setCustomerId(String customerId) {
        this.customerId = customerId;
    }

    public SubscriptionPlan getPlan() { ... }

    public void setPlan(SubscriptionPlan plan) { ... }

    public SubscriptionStatus getStatus() { ... }

    public void setStatus(SubscriptionStatus status) { ... }

    public LocalDate getStartedAt() { ... }

    public void setStartedAt(LocalDate startedAt) { ... }

    public LocalDate getExpiresAt() { ... }

    public void setExpiresAt(LocalDate expiresAt) { ... }

    public BigDecimal getPrice() { ... }

    public void setPrice(BigDecimal price) { ... }
}

There isn’t much here.

The Subscription holds data.

The business rules live somewhere else:

public class SubscriptionService {

    public void cancel(Subscription subscription) {
        if (subscription.getStartedAt().plusDays(7).isBefore(LocalDate.now())) {
            throw new IllegalStateException(
                "Free cancellation is only available during the 7-day trial"
            );
        }

        subscription.setStatus(SubscriptionStatus.CANCELLED);
    }

    public void changePlan(
        Subscription subscription,
        SubscriptionPlan newPlan
    ) {
        if (newPlan == subscription.getPlan()) {
            throw new IllegalArgumentException(
                "Subscription is already on this plan"
            );
        }

        subscription.setPlan(newPlan);
        subscription.setPrice(newPlan.getPrice());
    }

    public void renew(Subscription subscription) {
        if (subscription.getStatus() != SubscriptionStatus.ACTIVE) {
            throw new IllegalStateException(
                "Only active subscriptions can be renewed"
            );
        }

        subscription.setExpiresAt(
            subscription.getExpiresAt().plusMonths(1)
        );
    }
}

At first, this doesn’t look terrible.

But keep adding business rules.

Soon you have:

SubscriptionService
├── startTrial()
├── cancel()
├── changePlan()
├── renew()
├── pause()
├── resume()
├── extendTrial()
├── changeBillingCycle()
├── updatePaymentMethod()
└── ...

And your Subscription is still just data.

That’s where the real problems appear:

• Rules get scattered across services

• The same rules get duplicated in different places

• Rules can be bypassed by calling setters directly

• Changing a business rule means hunting through services to find everywhere it is implemented

• The Subscription object can’t protect its own state

✅Your Domain Owns the Rules (Rich Domain)

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When there are hundreds of things to check, QA can't check everything.

“That feature hasn’t changed.”

“Let’s just test the critical flows.”

Some tests get skipped.

That’s when production bugs slip through.

❌ “Just hire more QA”

But every release adds more features.

More features = more things to regression test.

QA can’t keep up.

❌ “But we already automated!”

You tried e2e tests.

Run it → red.

Run it again → green.

Eventually, nobody trusts the tests.

Developers start ignoring them.

You tried unit tests.

They're green.

But something still breaks in production.

Why?

Does Checkout Work?

Checkout has these requirements:

• Customer can add a product to the cart.

• Customer can pay by card.

• Order is created after payment.

• Customer can’t order an out-of-stock product.

⚠️Unit tests

Test individual pieces, but not the whole flow.

CartService adds a product.

PaymentService charges the card.

OrderService creates the order.

They can all pass while the checkout flow still breaks.

⚠️E2E test

It tests the whole flow.

But the behaviour is buried inside UI steps.

Open checkout → add product → enter card → click Pay → see “Order confirmed”

So what is this test actually checking?

Can the customer pay by card?

Is an order created?

Is the cart emptied?

Does the confirmation page appear?

You have to read the steps and figure it out.

Now imagine 200 tests like that.

You can’t easily tell:

“Which test proves that an out-of-stock product can’t be ordered?”

“Do we have a test for refunds?”

“Do we test that an order is only created after successful payment?”

✅ Acceptance test

The behavior being checked is obvious.

Given a product is out of stock
When a customer tries to order it
Then the order is rejected

The requirement and the test are the same.

You can see exactly what behavior you’re checking.

And because the test isn't tied to the UI, changing the checkout screen doesn't mean rewriting the test.

ATDD in Legacy Code: catch regression bugs before production.
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You already know a fat service class is a bad place for all your business logic.

So you split it up.

One class per use case, like in Uncle Bob's example:

public class PlaceOrder {
    public PlaceOrderResponse placeOrder(PlaceOrderRequest request) { ... }
}

public class ViewOrderDetails {
    public ViewOrderDetailsResponse getOrder(String orderNumber) { ... }
}

public class CancelOrder {
    public void cancelOrder(String orderNumber) { ... }
}

public class PublishCoupon {
    public Coupon createCoupon(String couponCode, BigDecimal discountRate,
                                Instant validFrom, Instant validTo, Integer usageLimit) { ... }
}

public class BrowseCoupons {
    public List<Coupon> getAllCoupons() { ... }
}

Looks better.

But splitting the service into separate classes didn’t fix everything.

Here’s what’s still wrong:

❌ Entities leak across the boundary — but only sometimes

PublishCoupon returns a Coupon.

BrowseCoupons returns List<Coupon>.

But PlaceOrder and ViewOrderDetails return response DTOs.

So... can entities cross the use case boundary or not?

You can’t tell. It depends on which class you’re looking at.

❌ A bug is hiding in PublishCoupon

If you accidently swap validFrom and validTo — it still compiles, because they’re the same type.

But now your coupon has the wrong validity period — it’s backwards.

CouponController already has a PublishCouponRequest with all the values. Instead of passing that request to PublishCoupon, it pulls the values out and passes them one by one.

That's how you end up with a bug like this.

❌ Expected outcomes are thrown, not declared

Order not found, order can’t be delivered — are thrown as exceptions instead of declared.

These aren't bugs. They're things that can happen when the use case runs.

But they’re in a shared exception handler that has grown a case for every use case's failures.

The method says “I return an order.” It doesn't tell you that “order not found” or “order can't be delivered” can happen.

❌ Calling each use case is different

PlaceOrder takes a request object, ViewOrderDetails/CancelOrder take a raw String, PublishCoupon takes five loose parameters, BrowseCoupons takes nothing.

There’s no obvious answer to: “How do I call a use case?”

❌ The class says one thing, the method says another

ViewOrderDetails.getOrder()

The class says “view the order details”, but the method says “get the order.”

Same problem here: PublishCoupon.createCoupon()

The class says “publish a coupon”, but the method says “create a coupon”.

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You open a PR labelled:

“API redesign — no behavior change”

Looks fine.

Merged.

The one-line acceptance test change goes unnoticed.

Six months later, a regression bug slips through.

The acceptance test that was supposed to catch it didn’t.

Why?

Because the test was quietly changed at the same time as the code.

That’s the problem.

If the behavior didn't change, the acceptance test shouldn't need to change.

The driver should handle it.

✅The test stays the same — the driver changes

Look at PlaceOrderPositiveTest.java in the shop’s system-test module:

@TestTemplate
@Channel({ChannelType.UI, ChannelType.API})
void shouldCalculateBasePriceAsProductOfUnitPriceAndQuantity() {
    scenario
            .given().product()
                .withUnitPrice(20.00)
            .when().placeOrder()
                .withQuantity(5)
            .then().shouldSucceed()
            .and().order()
                .hasBasePrice(100.00);
}

This test has no idea whether the order is placed by POST /api/orders or POST /api/purchases.

It has no idea whether the UI’s place-order button is selected by [aria-label="Place Order"] or by some new CSS class.

It also has no idea whether the ERP returns the product’s price as price or unitPrice.

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“AI can write the tests.”

Finally.

All those tests nobody has time to write.

No more days spent waiting for manual QA before every release.

Six months later…

More code.

More PRS.

But releases aren’t faster.

QA is still testing everything manually before anything ships — exactly as they were last year.

And the bugs? Still there.

Now management wants to know:

What did we actually get for the money?

You have plenty of numbers to show them.

More code.
More PRs.
More tests.

But none of those numbers answer the question.

Because more code isn't the same as safer releases.

AI copies whatever it finds

Ask AI to write a test.

But what if your e2e suite is full of tests coupled to the UI?

Click the button.
Fill the field.
Check the screen.

AI just writes hundreds more tests like that.

Faster.

It doesn't stop and ask:

“Is this a good way to test this application?”

It looks at the tests you already have and learns:

“This is how tests are written here.”

You had a speed limit

Before AI, writing tests was slow.

That was annoying.

But it also limited how many bad tests you could create.

Your team could only write so many brittle E2E tests before the sprint ended.

AI removed that limit.

Now you can write hundreds in the time it used to take to write a few.

But the tests aren’t any better.

They’re still coupled to the UI.

Still break when the UI changes.

Still create more work for the team.

Before AI writes 100 more tests

AI writing tests isn’t the problem.

AI writing bad tests faster is.

The problem is giving it a bad test suite to learn from.

❌ If your tests are all coupled to the UI, AI will keep generating tests like:

click("Place Order")

✅ Instead, fix the test, so that it's coupled to the domain rather than the UI:

shop.placeOrder()

Now AI can do what it’s good at:

Write hundreds more. Fast.

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“What should I do when a use case fails?”

But a use case can fail for completely different reasons.

Maybe:

• the customer doesn’t exist

• the order hasn’t been paid for

• the order cannot be checked out

• the payment provider is unavailable

Should the use case handle all of them?

Error Handling at the Application layer

This layer contains your use cases:

• checkout order

• refund payment

• create shipment

• reserve seat

This is where application rules are enforced.

Examples:

• cannot checkout if another checkout is already in progress

• cannot place an order for a customer that doesn’t exist

• cannot apply the same coupon twice

These are rules that the use case needs to check because because the information isn't inside the Order itself.

E.g. an Order can’t know whether the customer exists or another checkout is already in progress, or whether a coupon had already been used. The use case can check that information.

These are not technical failures. A database connection dropping is a completely different kind of problem.

❌ Use cases should NOT handle infrastructure exceptions

public void checkout(Order order) {

    try {
        paymentGateway.charge(order);
        orderRepository.save(order);
        shippingGateway.notifyWarehouse(order);

    } catch (StripeException e) {
        throw new RuntimeException("Payment failed");
    } catch (SQLException e) {
        throw new RuntimeException("Database error");
    } catch (HttpException e) {
        throw new RuntimeException("Warehouse call failed");
    }
}

Now the use case is tied to three unrelated technologies:

• StripeException — Stripe’s SDK

• SQLException — JDBC / the database

• HttpException — the HTTP client

Swap Stripe for PayPal, JDBC for an ORM, or the HTTP client for a message queue, and you now have to change the use case too.

But the checkout rules haven’t changed.

That’s the problem.

✅ Use cases should ONLY handle application errors

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Someone can spend 20 years getting better at software engineering.

Or they can spend 20 years repeating the same mistakes.

The “I’ve Been Doing This for 20 Years” Developer

There’s a particular kind of senior engineer who is difficult to work with.

They’ve been doing this for a long time.
They’ve seen everything.
They have an answer for everything.

And every new problem gets solved with something they’ve already done before.

You suggest a different approach.
They tell you why it won’t work.

You show them an example.
They explain why that example is different.

You point out a problem with their approach.
They tell you they’ve been doing this for 20 years.

And that’s supposed to end the discussion.

Experience Doesn’t Fix Bad Habits

It makes them harder to see.

A developer who has spent 10 years writing huge classes doesn’t necessarily become better at design.

They might simply become extremely fast at writing huge classes.

A developer who has spent 10 years avoiding tests doesn’t become good at testing.

They become very experienced at working without tests.

And someone who has spent years solving every problem with another layer, abstraction, framework, or design pattern can become incredibly experienced at overengineering.

Experience makes you better at whatever you repeatedly practice.

Including the wrong things.

Stop Counting Years

Years of experience are a terrible measurement of engineering ability.

A developer with three years of experience who constantly learns, experiments, gets feedback, and improves their approach can be far more effective than someone with fifteen years who stopped learning five years ago.

I’ve seen developers with relatively little experience who can walk into an unfamiliar codebase and start asking the right questions.

And I’ve seen extremely experienced developers who can’t change their approach even when it’s clearly not working.

Good Engineers Say “I Was Wrong”

The best engineers can look at their own code and say:

“I wouldn’t write this that way today.”

Years of experience can tell you how long someone has been in the game.

It doesn’t tell you how well they’ve played it.

Getting better means being willing to rethink how you’ve been doing things.

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The first time you see Hexagonal Architecture, it can feel like a lot.

Schedule a shipment.

You're used to something like: ShipmentHttpController, talking straight to the ORM and the FedEx API. One class, two integrations.

Then you look at the Hexagonal Architecture version of the same feature:

• driving adapter (ShipmentHttpController)

• driving port (ScheduleShipmentUseCase)

• the hexagon itself (Shipment, Address, PackageDetails, ShipmentRepository, CourierGateway)

• driven adapters (SqlShipmentRepository, FedExCourierGateway).

And here’s where things can get out of hand.

You start creating all of these classes before you actually need them.

The Starting Point

🚫 Problem: No pain yet

One feature, one caller, one integration on each side.

✅ Solution: Ship it as a single class

There’s nothing to abstract until something actually hurts.

One class handles the HTTP request, runs the validation, talks to Postgres through JPA, and calls the FedEx SDK directly. No ports, no adapters, no domain model.

This isn't the design we'd want to keep forever: it’s slow to test and it’s going to get worse as more logic lands in it — but it hasn’t gotten worse yet.

Step 1 — Driven Ports Keep I/O Out of the Way

🚫 Problem: Tests are slow

Every test hits a real Postgres instance and the real FedEx test instance, so they’re slow, and the test instance goes down more often than the code changes.

✅ Solution: Use driven ports for I/O

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🔒Hello, this is Valentina with a premium issue of the Optivem Journal. I help Engineering Leaders & Senior Software Developers apply TDD in Legacy Code. This article is part of the TDD in Legacy Code series.

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Component Tests are fast because they stub their dependencies

Component Tests give us fast feedback by testing a component in isolation.

The Frontend Team stubs out the Backend in Frontend Component Tests in Legacy Code; the Backend Team stubs out the External System (the ERP) in Backend Component Tests in Legacy Code.

But your stubs might be wrong!

When the Frontend stubs the Backend, it makes an assumption: “POST /api/orders returns 201 with { orderNumber }.”

But what if the Backend Team renames that field, or changes the status code, or moves the endpoint. What happens to the Frontend’s Component Tests?

They stay green. They’re still testing against the stub which hadn’t changed — but the stub’s assumption is now wrong.

The bug doesn’t show up until the Frontend and the Backend actually run together — when the Acceptance Test fails.

How to catch stub mismatch? Contract Tests

The Frontend says:

“This is what I expect from the Backend.”

Pact turns that expectation into a contract.

The Backend then runs the contract against itself.

If the Backend no longer matches what the Frontend expects, the Backend build fails.

You don’t write a new test from scratch. You take a Frontend Component Test you already have, and replace the stub with a contract.

Here are the steps to introduce Contract Tests in Legacy Code. You’ll get tasks to implement in your GitHub Sandbox Project. ⬇️⬇️⬇️

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“Architects don’t code.”

I’ve heard this sentence too many times.

And every time, I worry.

Because the moment someone thinks coding is beneath them, they are already moving away from the thing they’re supposed to understand.

The code.

The good ones

The best architects I’ve worked with never stopped writing code.

They reviewed pull requests.

They paired with developers when something got stuck.

They built the first few features of what they designed.

They understood the pain developers faced because they experienced it themselves.

"Coding is below me"

In many companies, an “architect” is what you get when there is nowhere else to go except management.

The architect stops coding. Not because the job doesn’t need it, but because the title says they’ve outgrown it.

Then the calendar fills up. Meetings, roadmaps, steering committees.

A year later they are deciding how the system should be built, without knowing how it is built.

“But nobody writes code by hand anymore”

This is where someone brings up AI.

If developers are generating most of the code, why should an architect write any?

Because typing the code was never the point. Knowing how the system is actually built was.

You can know that without writing every line yourself. You can’t know it without reading any of it.

Someone still has to notice that logging is now done three different ways in three different places. Or validation. Or error handling. Nobody told the model how this codebase does any of it, so it picked something reasonable each time.

What you want is consistency in the design itself: the same kind of problem solved with the same structure everywhere, so that a developer who has read one part of the codebase can predict what the next part looks like. No diagram gives you that. It comes from someone reading the code and saying, we already have a way of doing this, use that one.

That work is closer to the code than a diagram ever gets you. An architect who only reviews the design, and never looks at what came out the other end, is exactly as far away as before. The tooling is just newer.

If anything, AI makes it worse. More code arrives, faster, and less of it has been thought about by anyone. Judging whether it fits the system is more work than it used to be, not less.

Don’t become an ivory tower architect

The dangerous architect is the one who designs a system they would never have to maintain.

There is no way to find out what a design costs except to build something in it. Add a feature. Change one that already exists. Fix a bug in it six months later. That’s when you find out which parts of your own design fight you — the layer you have to touch every time, the abstraction nobody can extend without asking you first, the test that takes ten lines of setup before it can assert anything.

And don’t expect anyone to report it back to you.

Developers mostly won’t tell you the design is painful. You’re the architect, you decided it, and to them it’s a given — something to work around, not something to question. The more junior they are, the less likely you are to hear about it at all. What comes back is “it took a bit longer than we thought.”

If you haven’t opened the codebase in months, you’re not designing the system anymore.

You’re describing one.

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The first rule Uncle Bob lists in The Clean Architecture is:

“Independent of Frameworks. The architecture does not depend on the existence of some library of feature laden software. This allows you to use such frameworks as tools, rather than having to cram your system into their limited constraints.”

Frameworks as tools. That’s reasonable.

But it usually gets repeated as something much stricter.

No annotations. No libraries. No framework classes.

Nothing.

The domain must be completely pure.

I disagree.

Not because frameworks should control your business logic.

They shouldn’t.

But not every framework dependency is automatically a problem.

Some dependencies genuinely make your code harder to change.

Others simply make your code easier to write.

There’s a huge difference between:

• @Service

• @Component

• @Repository

and

• @Entity

• @Id

• @Column

• @ManyToOne

The first barely affects your domain. The second fundamentally shapes it.

The strict reading treats them the same.

A class with an annotation, a service managed by Spring, a repository injected by a framework — all bad.

The result?

They end up building systems that are technically “pure” but harder to work with.

Because the goal of architecture is not to remove every dependency. It is to remove the dependencies that dictate how your business rules are written.

A framework dependency is not automatically harmful

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When I started my career in software development, I thought I was surrounded by the wrong people.

Nobody wanted to write tests.

Nobody cared about clean code.

Every conversation about improving the codebase turned into an argument.

I remember thinking:

“If I just join a better company, things will be different.”

The next company was supposed to be different

I moved to another company.

Surely this time it would be different.

More experienced developers.

Better engineers.

Except...

The same problems were there.

“Tests are a waste of time.”

“We’ll clean up the code later.”

“We have always done it this way.”

Being a Senior Developer didn’t change anything

As a Senior Developer, I though:

“Now I can finally influence how we build software.”

We would write better code.

We would add tests.

We would stop rushing changes into production.

I was wrong.

Even getting people to write one test was a battle.

“Why would we write twice as much code?”

“Now we have to maintain the application code and all the tests too?”

The smaller company as a Tech Lead

The next move was to a smaller company, as a Team Lead.

This time I did things differently.

I stopped waiting for permission. In my own time, over several months, I built a Clean Architecture template — layered, testable, with unit tests already in place.

When I recruited developers for the team, I stopped screening only for years of experience. I screened for whether they cared about quality.

Then I built the first module with the template myself, end to end, so there was something real to point at.

And then I trained the team to do it the same way.

Six months later

Within six months, that team had shipped more features than the “senior” developers at my previous companies had managed in years.

Same industry. Same kind of problems.

The difference wasn’t that these developers were smarter. It was that nobody had to be convinced. The template made the right way the easy way, the hiring made the standard shared, and the training made it something the whole team could do — not something one person kept arguing for.

That’s what I had been getting wrong for years.

You don’t change a team by caring harder than everyone else. You change it by building the thing, showing it works, and bringing people with you.

The template was what made that possible. So that’s what I want to show you how to design.

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