Requirements Engineering and Software Testing - Richard Seidl

Requirements are the foundation on which our testing activities are based. And if we take a closer look during testing, it quickly becomes clear that test cases are a hidden superpower that thoroughly put our requirements through their paces.

Requirements: The solid foundation

Nobody would build a house without knowing where the doors and windows should go. It’s the same with software: if our requirements are half-baked, then the test - however good it may be - will be more like a happy treasure hunt. We may test in the wrong direction or forget important aspects. But if the requirements are precise, understandable and comprehensible, we have the ideal foundation for an efficient test phase.

And this does not necessarily have to be a detailed requirements specification. It can also be user stories with acceptance criteria, diagrams or scribbles. What counts in the end: Is a similar picture of the requirements anchored in everyone’s mind!

When testing becomes a review

The great thing about testing is that every time we design test cases, we unconsciously or consciously scrutinize the requirements again. Is everything logical? Are important scenarios missing? Does the requirements text match the desired results? In this way, every test case design also becomes a review of the requirements! This is an advantage that we often underestimate, as errors can be identified early and cost-effectively. And as testers, we are of course particularly critical here. If we can’t derive good test cases, we have to clarify the gaps and ambiguities. Architects or developers tend to fill the gaps automatically through creativity and logical reasoning. The tester’s only option is to ask the requester

AI: New playmate in the requirements check

But we now have support - the digital joker, so to speak: artificial intelligence. It gives us exciting opportunities to check and improve requirements. AI is becoming a static analysis tool. A few examples:

1. automatic consistency check: The AI analyzes requirements for potential contradictions. Perhaps feature A is mandatory according to requirement X, but conflicts with requirement Y.
2. linguistic clarity: An AI can recognize linguistic ambiguities, for example ambiguities or filler words that leave a lot of room for interpretation. In this way, we are warned of vague formulations at an early stage.
3. completeness check: No requirement should go unnoticed. AI-based tools can sniff out gaps and thus ensure greater completeness in the specification - ideal for preventing later headaches in the test phase.
4. traceability & impact analysis: If a requirement changes, the AI rolls the thread through all affected areas. It shows which other features and tests are affected. Transparency without manual sifting!

In a world where software is moving faster and faster and project risks are constantly increasing, good requirements are the be-all and end-all for successful testing - and for a successful project! Requirements engineering and software testing work hand in hand here - two sides of the same coin. And with AI at our side, good collaboration becomes even more efficient and effective.