The Two-Minute Failure Forecast: A Pre-Coding Ritual to Predict Your Next Bug Before It Exists

There’s a moment right before you start typing new code: the ticket is open, the spec is in front of you, your editor is ready. Most of us dive straight in.

What if you used those first 120 seconds for something else: predicting your next bug before it exists?

That’s the idea behind a two-minute failure forecast—a tiny, structured premortem you run right before you code. It doesn’t replace testing, reviews, or tools. It’s a lightweight ritual that sharpens your thinking, aligns you with the spec, and helps you spot failure modes early enough that they’re still cheap to fix.

From Postmortem to Premortem: Flipping the Script

We’re used to postmortems: something went wrong in production, so we analyze the incident, document root causes, and promise to “do better next time.”

A premortem flips that idea:

Instead of asking “What went wrong?” after failure, you ask “What could go wrong?” before you start.

In a premortem, you imagine that your project, feature, or release has already failed spectacularly, and you brainstorm all the ways that could have happened. This:

Surfaces hidden risks
Exposes incorrect assumptions
Encourages candid discussion about weaknesses

You can apply the same idea at a micro scale: a pre-coding premortem that takes two minutes.

What Is a Two-Minute Failure Forecast?

A two-minute failure forecast is a quick ritual you perform before writing or refactoring code:

Assume the code you’re about to write has already failed in production.
List the most likely reasons why.
Decide one or two simple things you’ll do right now to reduce those risks.

That’s it. No documents, no long meetings. Just 120 seconds of focused, structured pessimism.

This small pause changes how you approach the task. Instead of only thinking, “How do I make this work?” you also think, “How might this break?”

A Simple Script: How to Run a Two-Minute Failure Forecast

You can do this silently, in your head, or as a quick conversation in a pair/mob session. Here’s a concrete script you can follow.

Step 1: Anchor on the Specification (30 seconds)

Before you can imagine failure, you need to know what success looks like.

Ask yourself:

What is this code supposed to do? (inputs, outputs, constraints)
What are the edge cases? (empty lists, null values, timeouts, huge payloads)
What assumptions am I making about other systems or data?

If the spec is fuzzy, that’s your first red flag. Writing or clarifying a short specification (even a few lines in the ticket or docstring) gives you a reference point for failure forecasting.

No clear spec → no clear way to predict or detect failure.

Step 2: Imagine It Already Failed (45 seconds)

Now, assume your new code shipped and caused a real problem: data loss, bad output, performance meltdown, or a broken user flow.

Ask:

Where is this most likely to fail?
- Input validation?
- Boundary conditions (off-by-one, index errors)?
- Concurrency or race conditions?
- Integration points (APIs, databases, queues)?
- Error handling and retries?
What bug would be most embarrassing or expensive?
What would a future postmortem say was the root cause?

Write down (or mentally note) 3–5 likely failure modes. Don’t try to be exhaustive—speed matters. Aim for realism, not perfection.

Step 3: Choose Cheap Defenses (45 seconds)

For each likely failure, quickly ask:

What is the smallest thing I can do now to lower this risk?

Common answers:

Add or expand a unit test for a specific edge case
Add a guard clause or input validation
Use a known-safe pattern instead of a clever hack
Add a comment or TODO capturing a risk you can’t fix now
Mark something for a targeted code review (e.g., “pay extra attention to the date math”)

Pick one or two actions you’ll commit to immediately. Keep it small—if this ritual becomes heavy, you’ll stop doing it.

Why Coding Conventions and Linters Make Forecasts Stick

A failure forecast is only as good as your follow-through. This is where coding conventions and automated tools shine.

When you forecast failure modes like:

“Someone will forget to handle null here.”
“We’ll have inconsistent date formats between modules.”
“This function will grow into an unreadable mess.”

You can often neutralize them by enforcing conventions, either:

Manually via code review: reviewers check a clear, agreed style and reliability checklist.
Automatically via tools: linters and formatters (ESLint, Pylint, Prettier, Checkstyle, etc.) catch the low-level inconsistencies and anti-patterns.

Conventions and linters turn your forecast from a wish list into a systematic safety net.

Instead of hoping people remember the things you just thought about, you:

Codify them as rules, and
Automate enforcement where possible.

This dramatically reduces the chance that your predicted failures will actually occur.

Specifications: The Map for Your Forecast

Your two-minute forecast is anchored to this question:

“Failure relative to what?”

If you don’t have a clear specification, you’ll either:

Forecast trivial failures (that don’t matter), or
Miss important mismatches between expectations and behavior.

Writing a clear, minimal program specification—even something like:

A short list of input types and constraints
A table of examples: input → expected output
Behavioral rules (e.g., “If the API is down, cache the last known value for 5 minutes”)

gives you a concrete target. You can now ask:

“Where is this spec ambiguous?”
“Where might my code diverge from this spec?”
“What spec rule is easiest to accidentally violate?”

Those questions are the heart of an effective failure forecast.

Humans and Machines: Forecasting Bugs Before Compilation

Modern development tools increasingly act like automated co-pilots in your premortem:

IntelliCode, GitHub Copilot, and similar tools suggest patterns based on massive codebases. They implicitly encode what tends to work and sometimes surface what looks suspicious.
Static analysis tools and IDE inspections flag bug-prone patterns (null dereferences, unused variables, suspicious comparisons) even before you compile or run tests.

Think of these tools as automated participants in your two-minute forecast. They:

Highlight places where other developers have historically made mistakes.
Point out risky constructs as you type.

Your job is to combine tool feedback with human judgment:

Use the forecast to decide where to slow down and think harder.
Use the tools to catch mechanical and low-level issues at scale.

Together, you get a richer, earlier view of potential failure.

Making It Work in Real Teams: Lightweight, Not Bureaucratic

In industrial settings, anything that sounds like “a new process” is a risk in itself. Will it slow us down? Will people ignore it after two sprints?

The key is to keep the failure forecast lightweight and integrated:

Add a single checklist item to your definition of ready or pull request template:
- “Did you perform a 2-minute failure forecast? List top 3 risks.”
Use it as a conversation starter in code reviews:
- “What did you identify as likely failures before coding? Did we address them?”
Incorporate it into pair programming:
- Spend the first 2 minutes of a session doing a shared forecast on a whiteboard or shared doc.

Standards like ISO 29110, aimed at very small entities, explicitly value lightweight process improvements—simple, low-overhead steps that improve predictability and quality without drowning teams in paperwork.

A two-minute failure forecast is a perfect example:

It’s structured, repeatable, and teachable.
It adds discipline and risk awareness.
It fits neatly into lean and agile processes.

You get process maturity without heavy ceremony.

Putting It All Together: A Sample Micro-Ritual

Here’s how this might look in practice for a developer starting a new ticket.

Clarify the spec (30s):
- “I’m writing a function that calculates discounts. Inputs: customer type, cart total, coupons. Output: final price. Rules: no negative totals, VIP rules override coupons.”
Forecast failure (45s):
- “Most likely failures: incorrect rounding, missing an edge case when total is zero, conflicting rules between VIP and coupons, performance issues if called in a tight loop.”
Plan defenses (45s):
- Add unit tests for zero total and max discount.
- Add a clear priority rule in comments and tests (VIP > coupons).
- Use existing money/decimal utility instead of rolling my own.
- Mark in PR description: “Please sanity-check discount precedence logic.”
Reinforce with tools and conventions:
- Rely on linter/formatter to keep style consistent.
- Let static analysis warn about unhandled branches.

You’ve just invested two minutes to steer hours of work in a safer direction.

Conclusion: Make Failure Prediction a Habit, Not a Hero Move

Most bugs don’t come from a lack of intelligence—they come from a lack of deliberate foresight.

By adding a two-minute failure forecast before you code, you:

Turn vague worries into concrete, actionable risks
Tie your work more tightly to clear specifications
Use coding conventions and tools to reduce recurring failures
Fit a meaningful risk practice into even the busiest industrial workflows

You don’t need a new committee, a heavyweight process, or a thick binder of procedures. You just need 120 seconds of honest pessimism and a willingness to act on what you see.

Next time you open your editor for a new task, pause. Before the first line of code, ask yourself:

“If this has already failed in production, what went wrong—and what can I do right now to prevent that?”

Then start typing—with your future postmortems already a little less painful.