The Most Troublesome Software Bug: Not a Crash, but Clear Requirements That Still Produce the Wrong Result

The most troublesome software bug is not a sudden crash – so what is it?

Previously, we said that if requirements are unclear, AI may build the software wrong.
But now, let us discuss a more troublesome problem:

If the requirements have been written perfectly clearly and AI follows them, why can the result still be wrong?

For example.
An e-commerce enterprise wants to develop an order system, and the rules are already written very clearly: spend 300 yuan and get 50 yuan off, members enjoy additional discounts, users can use coupons, and when part of an order is returned, the refund amount must also be calculated according to the rules.

Can AI build these features?
Of course it can.
And it may write out all the code very quickly.

So where does the problem lie?

Suppose a customer buys two items with a total price of 400 yuan, uses the spend-and-save discount, and also uses a coupon.
A few days later, they apply to return one of the items.
At this point, how much should be refunded?

Should it be refunded at the original price? At the actual amount paid after discounts? Since the spend threshold is no longer met, should the price of the other item be recalculated? The coupon has already been used – can it be restored?

If the order also involves points deduction, member discounts, or returns of multiple items separately, the calculation rules become even more complex.

Note that every one of these rules can be written clearly in advance.
But once the rules are combined, they generate a large number of different business scenarios.
AI may implement every individual feature, yet miss a certain rule combination; or it may calculate correctly in ordinary scenarios but get it wrong under some special condition.

What is even more troublesome is that the program may not report an error at all.
The database shows no exception, the interface returns normally, and the page successfully displays the refund amount.

But the money is calculated wrong.

This is what is truly difficult about complex business systems.
Because software is not just about stacking features one on top of another. Every feature has associations, dependencies, and mutual influences with others.

01.As Long as Rules Are Sufficiently Defined, AI Can Also Handle Complex Business

Of course, this is not to say that AI cannot handle complex business. As long as the rules are sufficiently defined, testing coverage is adequate, and independent verification is combined, AI can also help developers discover and solve a large number of problems.

But the question is:

How do you know which situations have not yet been tested?And how do you prove that scenarios not appearing in the test data can also be handled correctly?

This requires developers to understand the relationships between business rules, design boundary conditions, verify results under different scenarios, and, when necessary, perform cross-checks through independent calculations or business rules.

02.A Pitfall Worth Being Vigilant About in the AI Programming Era

So in the AI programming era, a pitfall worth being vigilant about is:

assuming that once the features are implemented and the tests pass, the software is already correct.

Truly reliable software must not only prove that it can run under normal conditions, but also prove as far as possible that all important boundary conditions and abnormal scenarios are handled correctly.

Writing features answers what the software can do;verifying business correctness answers whether we can trust it.

03.It Becomes Even Harder When Modifying Legacy Systems

And this becomes even harder when modifying legacy systems.
Because you must not only ensure the new features are correct, but also ensure that after the changes, the originally inconspicuous features that have always worked normally do not suddenly break.