What happens when performance requirements aren't specified correctly, and why it matters for software delivery.

Performance requirements are the compass for a software project. If they’re fuzzy or off, you don’t just risk a detour—you risk arriving somewhere nobody wanted. In the world of software engineering, expectations about speed, capacity, and resource use shape design decisions, test plans, and even which features land in the release. When those expectations aren’t stated clearly, teams can chase the wrong targets, testers become frustrated, and stakeholders wonder why the product underperforms in real life. Let me pull back the curtain on what happens next, using a common question from the IREB Foundation Level domain as a guide.

A quick reality check: what happens when performance requirements aren’t specific?

Here’s the thing most folks in the room recognize quickly: if you don’t define how fast or how much load the software should handle, you’re setting the stage for disappointment. The multiple-choice options in many foundational scenarios hint at a simple truth. The most likely outcome is that the software will be rejected for providing insufficient performance. In plain terms: without measurable targets, the product can’t prove it meets the standards stakeholders rely on. It’s not just about speed in isolation; it’s about the whole package—the throughput, the response times, the predictability under pressure, and the way resources are managed as the user base grows.

Why does this rejection tend to happen?

Think about a demo that promises “fast enough” or a system that must “scale as needed.” Those phrases sound reasonable, but they’re vague. In practice, vague performance expectations create a few familiar frictions:

• Ambiguity breeds misalignment. Stakeholders, developers, and testers might all picture different things when they hear “fast.” One person might imagine pages that load in two seconds; another might be happy with five seconds under peak load. Without a shared metric, the software is built to a personal expectation, not a common standard.

• Real-world use differs from a sunny lab. Performance studies often happen in controlled environments. When the system runs in production, with real users, fluctuating networks, and diverse data, those lab numbers rarely hold up. If the top-level requirement is “high performance,” but there’s no threshold, it’s easy to miss the mark.

• Testing loses its teeth. If you can’t point to specific metrics, test plans drift. You end up with tests that check for “acceptable behavior” rather than “proven performance under X conditions.” The result is a long list of pass/fail results that don’t clearly map to business goals.

• Stakeholders lose confidence. When a system can’t demonstrate concrete performance, it’s hard to justify a release. In the worst case, the product gets rejected or delayed, and money, time, and trust take a hit.

All of this fits neatly with what IREB Foundation Level materials emphasize: requirements aren’t just a box to check; they’re a contract with the user and a blueprint for the team. If the contract lacks precise performance criteria, you’re not delivering a product—you’re delivering an idea.

How to write performance requirements that stand up to scrutiny

Now, let’s flip the script. If you want to avoid the rejection trap, set performance expectations that are clear, testable, and traceable. Here are practical steps you can apply, mixing a bit of the “engineering lens” with everyday business sense:

• Make them measurable, not fuzzy. Replace phrases like “fast” or “responsive” with numbers. For example:

• Mean response time under normal load: under 1.5 seconds.

• 95th percentile response time under peak load: under 2.5 seconds.

• Maximum concurrent users sustained without error: X users.

• Throughput: Y requests per second under specified data sizes.

These targets give developers a precise goal and testers a clear pass/fail criterion.

• Define the scope and the environment. Performance isn’t universal; it’s context-specific. Specify the hardware, network conditions, data volume, and typical user patterns where the targets apply. If you test in a sandbox, document how it maps to production, so the results aren’t misread.

• Tie to business value. Performance targets should connect to user outcomes. For example, “search results appear within 2 seconds for 90% of queries during peak hours,” aligns speed with user satisfaction and business impact.

• Include test criteria and methods. Don’t stop at “the system should be fast.” Say how you’ll measure it, what tools you’ll use, and what constitutes a pass. Example: “Performance tests run with JMeter simulating 1,000 concurrent users; the average latency must stay under 1.8 seconds, and error rate must remain below 0.1%.”

• Cover different conditions. Performance isn’t one number; it’s a blend. Consider load under:

• Normal day-to-day usage

• Peak times (special events, promotions)

• After long idle periods (warm-up effects)

• Failure modes (what happens if a subsystem slows down)

• Include reliability and resource constraints. Performance isn’t only speed. Add targets for:

• Availability (uptime percentage)

• Resource usage (CPU, memory, disk I/O boundaries)

• Recovery time after a failure

• Build in traceability. Link each performance requirement to a business objective or a user story. If a requirement says “mean response time <= 1.5s,” note which user journey this covers and why it matters. That traceability makes it easier to argue for or against changes later on.

• Make them engineering-friendly. Use consistent units and formats. If you’re using milliseconds, keep it consistent across the board. If you mix seconds and milliseconds, you create a new source of confusion.

• Plan for validation. A good requirement has a plan to verify it. Include a brief description of the tests, the data, and the acceptance criteria. In other words, build in the verification step from the start.

A practical checklist you can use tomorrow

If you want a quick-start checklist, here’s a lean version you can apply to any project scenario:

• Define at least one primary performance metric (response time, throughput, or a combination).

• State the acceptable range clearly (e.g., mean <= X ms, p95 <= Y ms, max concurrent users Z).

• Specify the environment (hardware, network, data size) where these metrics hold.

• Outline the test approach (tools, scenarios, data sets).

• Tie the targets to user goals or business outcomes.

• Add a verification plan and acceptance criteria.

• Include a rollback or mitigation step if targets aren’t met.

• Note any constraints or trade-offs (costs, power usage, maintenance overhead).

A quick, real-world lens

Imagine you’re building a web app for a busy retail site. The user journey centers on a product search, filter, and checkout. If the performance requirements simply say “the site should load fast,” you’re leaving room for interpretation. The product team might expect a 3-second page load in a calm setting, while a performance engineer optimizes for a spike of 10,000 simultaneous shoppers. Without precise metrics, you’ll get a debate during testing, rework after deployment, and ultimately a release that doesn’t satisfy either group.

On the flip side, if you specify targets like:

• 95th percentile page load for search results under peak load (10,000 concurrent users) ≤ 2.2 seconds

• Throughput of 1,000 search requests per second under load

• CPU usage never exceeds 75% on production-grade servers

• Memory footprint under peak load remains within 1.2 GB

you give the team an actionable map. The testers know what to simulate, the developers know what to optimize for, and the business side can see how performance translates into a smooth user experience and a happy customer.

The human side of performance targets

It’s easy to treat numbers as cold, hard facts. Yet performance work is partly about perception and trust. Stakeholders want to know that the product will perform in the real world, not just in a lab. Clear, testable targets reduce the tension between what’s desired and what’s delivered. They also reduce rework, which is a relief for anyone who’s ever faced a late-night debugging sprint caused by vague expectations.

Relatable analogy can help. Think of performance requirements as the fuel gauge, oil pressure, and mileage readout for a car. If you only say “the car runs well,” you won’t know when you’re low on fuel, burning oil, or running inefficiently at highway speeds. By naming exact fuel economy, expected maintenance intervals, and engine performance under stress, you give drivers—and the mechanics—a shared language. That shared language keeps the vehicle on track and the journey feeling predictable.

A final nudge of clarity

Here’s the core takeaway: when performance requirements aren’t specified clearly, the odds tilt toward rejection due to insufficient performance. That outcome isn’t a personal slight against the team; it’s a natural consequence of missing a concrete target. By making performance criteria measurable, testable, and traceable, you give everyone a reliable yardstick. You also protect the project from drift, protect user satisfaction, and protect the investment you’ve made in building something that people can actually rely on.

If you’re mapping out requirements in line with IREB Foundation Level concepts, aim to blend technical precision with business clarity. Note which metrics matter, how you’ll measure them, and what success looks like in real terms. And remember: the numbers you set today define the experience users will feel tomorrow. When those numbers are well-chosen, the product earns trust, speeds forward, and stays efficient as needs grow.

So, the next time someone mentions performance, bring the specifics. A clear, testable target isn’t just a checkbox—it’s the difference between a product that performs and a product that stalls. The outcome isn’t just technical: it’s about delivering a dependable, usable experience that people actually reach for.