It was a Thursday afternoon when the Teams message came in.
A bug in production. A date formatting issue causing checkout to fail for users in certain timezones. Small bug, real impact: the payment flow was broken for roughly 8% of users.
The fix took twelve minutes. A single condition, a timezone offset, a hotfix deploy.
Everyone was relieved. The PM sent a thumbs up emoji. The on-call engineer closed the ticket.
Six months later, that same timezone logic had been copy-pasted into four other places across the codebase. Each with its own slight variation. Each slightly wrong in a different way.
The bug that took twelve minutes to "fix" ended up costing the team three days of a proper refactor, plus two additional incidents in the meantime.
That's the hidden cost of "it works."
TL;DR
- Quick fixes feel like speed. They are actually deferred cost and the interest rate is brutal.
- The damage is dual: it hits the codebase (fragility, duplication, entropy) and the team (decision fatigue, false confidence, loss of standards).
- The solution isn't to never move fast. It's to develop a decision framework that tells you when a quick fix is acceptable and when it will cost you ten times its weight.
Table of Contents
- The Anatomy of a Quick Fix
- The Two Places Quick Fixes Do Damage
- The Broken Windows Effect in Codebases
- Why "It Works" Is the Most Dangerous Phrase in Engineering
- The Hidden Cost, Quantified
- The Quick Fix Decision Framework
- When Quick Fixes Are Actually the Right Call
- Building a Culture That Pays Debt on Schedule
- Final Thoughts
The Anatomy of a Quick Fix
Not all quick fixes are created equal.
Some are intentional trade-offs: "we know this isn't the right solution, we're doing it to ship, and we'll revisit it next sprint."
Others are accidental: "this solves the immediate problem, let's move on", with no intention of revisiting.
And then there's the most dangerous kind: the ones that start as intentional and silently become permanent.
Ward Cunningham, the person who coined the term "technical debt" in 1992, described debt as something you take on deliberately, with a plan to pay it back. The problem isn't the debt itself. The problem is when the plan to repay it disappears.
// "Temporary" quick fix. Added: March 2023.
// TODO: replace with proper timezone utility
const offset = user.country === 'IT' ? 2 : 0
const adjustedDate = new Date(date.getTime() + offset * 3600000)
If you've worked in any codebase for more than a year, you've seen comments like this. The TODO is still there. The "temporary" fix shipped two years ago.
The Two Places Quick Fixes Do Damage
Quick fixes are often discussed as a purely technical problem.
But the damage happens in two places, and only one of them shows up in your code.
In the codebase
This is the visible damage. Over time, quick fixes create:
- Duplication without intention: logic that should live in one place spreads across multiple files, each with slight variations.
- Fragile dependencies: workarounds that assume things about the system that aren't guaranteed to stay true.
- Entropy: the codebase slowly drifts from its original architecture. Modules that were designed to do one thing start doing three.
You've already seen this pattern described in Bad Code Is a High-Interest Loan: each shortcut adds interest that compounds over time.
Quick fixes are where most of that interest originates.
In the team
This is the invisible damage and it's often more costly.
Decision fatigue. Every quick fix that goes unaddressed adds a micro-decision to every future change: "should I work around this, or fix it properly?" Over time, this is exhausting.
False confidence. "It works" becomes the team's de facto quality standard. The bar for "acceptable" slowly lowers. Nobody notices because the shift is gradual.
Loss of standards. When quick fixes stop being exceptions and start being the norm, new team members learn that the norm is acceptable. Standards don't erode from the top down, they erode from repeated practice.
"Any fool can write code that a computer can understand. Good programmers write code that humans can understand."
- Martin Fowler, Refactoring
The hidden cost of quick fixes isn't just the code they leave behind. It's the culture they quietly teach.
The Broken Windows Effect in Codebases
In 1982, criminologists James Q. Wilson and George Kelling proposed the Broken Windows Theory: a broken window left unrepaired signals that no one cares, which invites more disorder. One broken window becomes ten.
The same dynamic plays out in codebases.
One unaddressed quick fix signals to everyone else that this kind of code is acceptable here.
Another quick fix lands nearby. Then another.
Nobody makes a deliberate choice to lower standards. But collectively, the team's behavior changes in response to what they see around them.
Robert C. Martin and colleagues described it in The Pragmatic Programmer:
"Don't leave broken windows (bad designs, wrong decisions, or poor code) unrepaired. Fix each one as soon as it is discovered."
This doesn't mean stop everything and refactor whenever you spot a problem. It means that leaving problems unaddressed always has a cost, even if that cost is invisible today.
Why "It Works" Is the Most Dangerous Phrase in Engineering
"It works" is a binary measurement.
It either works or it doesn't.
But software quality isn't binary. It exists on a spectrum, and "it works" tells you almost nothing about where on that spectrum your code actually lives.
A function can "work" and still be:
- impossible to understand without context
- brittle in edge cases nobody has hit yet
- completely untestable in isolation
- a ticking clock for the next developer who needs to modify it
The problem with "it works" as a standard is that it optimizes for the present and ignores the future. Every codebase "works", right up until it doesn't.
// This "works"
function getPrice(p: any, d: any, t: any) {
return p - (p * d) + (p * t)
}
// This also works and tells you what it actually does
function calculateCheckoutPrice(
basePrice: number,
discountRate: number,
taxRate: number
): number {
const discountedPrice = basePrice * (1 - discountRate)
const finalPrice = discountedPrice * (1 + taxRate)
return finalPrice
}
Both functions return the same number. Only one communicates intent. Only one is safe to modify six months from now by someone who wasn't there when it was written.
"It works" describes behavior. What you actually need is behavior plus clarity plus maintainability.
The Hidden Cost, Quantified
Let's make this concrete, because abstract arguments about code quality are easy to dismiss in a sprint planning meeting.
Here's a rough model of what quick fixes actually cost over time:
The 1-3-10 rule (based on the well-documented cost-of-change curve in software engineering):
| When the fix is made | Relative cost |
|---|---|
| During development (before merge) | 1x |
| After merge, in the same sprint | 3x |
| Weeks/months later, in production | 10x or more |
Every quick fix that lands in production without a remediation plan starts at 10x cost, minimum.
Now add compounding.
If that quick fix becomes the foundation for a new feature, the new feature inherits all of its fragility. Every subsequent change that depends on it pays interest too.
The timezone example from the opening of this article illustrates this precisely:
- 12 minutes to apply the quick fix.
- 2 incidents caused by copy-pasted variations of the same logic.
- 3 days to trace, consolidate, and properly fix all four instances.
That's roughly a 36x multiplier on the original time investment, not counting the user impact and the on-call stress.
The Quick Fix Decision Framework
Here's the tool I wish I'd had earlier: a simple framework for deciding whether a quick fix is acceptable, or whether you're about to create a future problem.
Before merging any quick fix, run through these four questions:
1. Is it documented?
A quick fix without a paper trail becomes permanent by default.
At minimum, leave a comment with:
- the date
- why this was chosen over a proper solution
- a link to the ticket for the future refactor
// QUICK FIX - 2025-06-12
// Using a hardcoded offset to unblock the checkout bug (INC-4421).
// Proper fix: centralize timezone handling in a utility module.
// Tracked in: https://linear.app/yourteam/issue/ENG-889
const offset = user.country === 'IT' ? 2 : 0
This comment costs you thirty seconds. It saves the next developer thirty minutes of archaeology.
2. Is it isolated?
A quick fix that touches a core abstraction is fundamentally different from one that lives in a leaf node of your architecture.
Ask yourself: "if this assumption turns out to be wrong, how many places break?"
If the answer is more than one: it's not a quick fix, it's a risk.
3. Does it have a scheduled remediation?
A quick fix without a ticket is just a bug you haven't found yet.
Create the ticket. Assign it. Don't let the sprint end without it existing in your backlog.
The fix doesn't have to be immediate. It has to be visible.
4. Does the team know about it?
Quick fixes made in isolation, under deadline pressure, on a Friday, without review, are the ones that become permanent.
A fifteen-second mention in standup ("I shipped a workaround for X, ticket ENG-889 tracks the proper fix") changes the dynamic completely. It creates shared accountability.
The decision matrix
Use this as a quick gut-check:
| Condition | Acceptable quick fix? |
|---|---|
| Documented + ticket exists | Yes, if isolated |
| Undocumented, no ticket | No |
| Touches a core abstraction | No |
| Isolated + low blast radius | Yes, with documentation |
| "We'll fix it eventually" (no date) | No |
| Team is aware + remediation is scheduled | Yes |
When Quick Fixes Are Actually the Right Call
This wouldn't be an honest article without acknowledging that quick fixes are sometimes the right decision.
There are moments where speed genuinely matters more than perfection:
Production incidents. When a bug is actively impacting users and revenue, stopping to architect the perfect solution is the wrong call. Ship the fix. Create the ticket. Come back.
Validated uncertainty. If you're building a feature you're not sure the product will keep, over-engineering it is waste. A deliberate quick fix on an experimental feature is good product thinking.
Clear blast radius. If the workaround is contained, isolated, and fully documented, the risk is manageable. Not every quick fix becomes a monster. Some stay small.
The difference between these and the dangerous kind isn't the code itself. It's whether you're making a conscious trade-off with a plan or an unconscious shortcut with denial.
As Kent Beck put it:
"Make it work, make it right, make it fast."
The problem isn't "make it work", that's always step one. The problem is treating step one as the finish line.
Building a Culture That Pays Debt on Schedule
Individual decisions matter. But so does the environment in which they're made.
If your team is consistently reaching for quick fixes, the problem usually isn't the developers. It's the system they operate in.
A few structural changes that make a measurable difference:
Allocate explicit time for debt reduction.
The teams that manage technical debt best treat it like any other work: they schedule it. A common approach is dedicating 15–20% of every sprint to debt reduction. Not "if we have time." Scheduled.
Make quick fixes visible in your backlog.
Every quick fix should produce a ticket. The backlog is the team's shared memory. If a workaround isn't in the backlog, it doesn't exist as far as future planning is concerned and it will never get fixed.
Normalize "good enough for now, better by [date]".
The goal isn't to eliminate quick fixes. It's to make them explicit. When a developer says "this is a quick fix, the proper solution is tracked in ENG-889, and we've agreed to address it in the next sprint", that's a healthy team.
Review quick fixes in retros.
Not to blame anyone. To learn. Which quick fixes turned into real problems? Which ones stayed contained? What made the difference? Patterns will emerge.
This connects directly to what we explored in Corporate Amnesia: teams don't lose knowledge in dramatic moments. They lose it in accumulation, one undocumented workaround at a time.
Final Thoughts
"It works" will always be a seductive phrase.
Especially at 5 PM on a Friday with a deadline looming.
But after years of working in codebases shaped by the accumulated weight of "it works," I've come to think of it differently.
"It works" isn't a finish line. It's a starting point.
The real question that comes after it is: "and will it still be safe to touch in six months, by someone who wasn't here when we wrote it?"
Every quick fix that answers "I don't know" to that question is a bet. Sometimes the bet pays off. Often it doesn't. And when it doesn't, you don't lose the twelve minutes it took to write, you lose the three days it takes to untangle.
Speed and quality are not opposites. The teams that move fastest over time are not the ones that cut the most corners. They're the ones that cut corners deliberately, visibly, and with a plan to fix them.
That's not perfectionism. That's how you protect your velocity.
What's the most expensive "it works" you've ever shipped?
Drop it in the comments, the more specific, the better. I'll start: mine was a hardcoded locale string that somehow ended up in twelve files before anyone noticed.
If this resonated, a ❤️ or a 🦄 helps more people find it.
And if you want to follow the series, hit follow, the next article is already in the works.
Top comments (48)
I’d push back on the ‘quick fix = bad’ framing. the 12-minute fix was the right call - broken checkout, paying users, you patch it. the real bug was six months later when it got copy-pasted into four other places.
Completely agree, the 12-minute fix itself wasn’t the problem.
In production incidents, especially on critical paths like checkout, restoring service fast is often the correct engineering decision.
The real cost appears when the temporary workaround silently becomes “the architecture” because nobody revisits it afterward, exactly like your copy-paste example.
That’s actually the distinction I was trying to make in the article:
A quick patch with a follow-up ticket, visibility, and a planned cleanup is healthy engineering. A quick patch that survives for years and spreads through the codebase is where long-term velocity starts to erode.
Your example captures that perfectly.
right on both — though I'd push back slightly on the framing that intent is what matters. teams that intend to revisit still don't, because the checkout patch works fine for 7 months until you try to add a new payment method. the workaround becomes architecture before anyone calls it that.
That’s a really important distinction.
A lot of teams do intend to revisit temporary fixes. The problem is that software systems reward whatever keeps working in the short term. Once the incident is over and the metric recovers, the patch stops feeling temporary, even if everyone originally treated it that way.
So you’re right: intent alone isn’t enough protection.
What actually seems to matter is whether the organization has mechanisms that force temporary decisions back into visibility:
Without those, “temporary” naturally hardens into dependency.
And the dangerous part is that it usually doesn’t fail immediately. It fails later, exactly when the business needs flexibility, like adding a new payment method, supporting regional logic, or scaling throughput. That’s when accumulated shortcuts suddenly reveal themselves as architectural constraints.
So maybe the better framing is:
quick fixes are operationally necessary,
but systems need deliberate pressure to prevent them from fossilizing into architecture.
the naming moment is the tell - when someone writes it into the runbook as 'our approach to X' you know the window closed. it's not a patch anymore, it's architecture.
Exactly!
yeah once it hits the runbook you're not debugging code anymore - you're negotiating with culture
Exactly, you fully got the point!
appreciate that — culture negotiation is the part that never shows up in postmortems but drives half the actual decisions. glad the framing landed.
Thank you @itskondrat
This hits a problem almost every engineering team eventually faces: optimizing for immediate delivery at the expense of future velocity.
What makes “it works” dangerous is that the cost is delayed and invisible at first. The feature ships, everyone moves on, and only months later do teams realize they’ve created a system that’s harder to change, harder to debug, and harder to trust.
I especially agree with the idea that long-term speed comes from maintainability, not shortcuts. Sustainable engineering isn’t about perfection — it’s about leaving the codebase slightly easier for the next person (or future you) to work with.
The best teams I’ve seen treat quick fixes like loans: sometimes necessary, but always tracked and intentionally repaid.
Really appreciate this perspective, @lucaferri.
You explained perfectly why this mindset becomes so dangerous over time: the consequences are rarely immediate, so teams often don’t notice the impact until the codebase starts resisting every change.
I also like your comparison between quick fixes and loans. Sometimes they’re necessary, especially under pressure, but the important part is acknowledging the debt and planning to pay it back intentionally instead of normalizing it.
Completely agree as well on sustainable engineering not being about perfection. In many cases, even small improvements in readability, structure, or maintainability can compound into huge gains for the whole team months later.
Thanks for sharing such a thoughtful comment.
Thanks Gavin, really appreciate the thoughtful reply.
I think one of the hardest parts is that technical debt often looks like productivity in the short term, so it gets rewarded unintentionally. Teams feel fast right until the moment every change starts taking twice as long.
What you said about small improvements compounding over time is key. Clean architecture rarely delivers a dramatic “wow” moment, but over months it creates stability, confidence, and much better decision-making speed across the team.
Really enjoyed the article — it captures a reality many developers experience but struggle to articulate clearly.
That’s such a good point.
Technical debt often disguises itself as efficiency because the negative effects arrive much later, usually when the context has changed or the pressure has increased. By then, teams are no longer optimizing for delivery, they’re spending energy fighting the system itself.
I also completely agree on clean architecture rarely being “visible” work. People notice flashy features immediately, but maintainability shows its value quietly through faster onboarding, safer changes, fewer regressions, and better confidence across the team.
Really appreciate your insights here, Luca. This is exactly the kind of discussion I was hoping the article would spark.
Thank you @gito3 :)
That Thursday-afternoon scene is the most common origin story for the kind of fix that becomes the architecture. The quick patch never gets revisited because nothing breaks again, and the next person inherits the dead code as load-bearing. I started writing a one-line 'why this is here' comment on every hot patch — not the fix description, the reason the proper fix did not happen. Six months later that comment is the only thing that lets the next engineer judge whether the original constraint is still real.
That’s an excellent practice.
I especially like the distinction between documenting what the patch does versus documenting why the proper fix didn’t happen. That context is usually what disappears first.
Most “mysterious legacy code” started as a reasonable constraint under pressure:
But six months later, nobody remembers whether that constraint still exists, so the workaround gets treated as intentional design.
Your “load-bearing dead code” phrasing is painfully accurate 😄
A small comment explaining the original trade-off can save hours of archaeology later and, more importantly, it gives future engineers permission to reevaluate the decision instead of preserving it indefinitely.
The "permission to reevaluate" framing is the part I keep underestimating in my own commits. The temporary trade-off comment isn't really for the next engineer's understanding it's an explicit unlock that future-you didn't lock the door behind you. Most workarounds calcify because the original constraint loses its expiration date, not because anyone trusts the workaround on its merits.
That’s such a sharp way to frame it.
I love the idea that documentation around a workaround isn’t just historical context, it’s a form of permission architecture. You're signaling to future-you (or the next engineer) that this was a conscious compromise, not sacred design.
“Temporary” fixes become permanent mostly because the original constraint disappears, but the workaround keeps looking intentional. Without an expiration date or explicit reevaluation trigger, people assume touching it is dangerous.
That’s also why I think the psychological side of technical debt matters as much as the technical one: undocumented compromises quietly become team norms.
Your “didn’t lock the door behind you” line captures that perfectly.
The "expiration date or reevaluation trigger" is the part I've been trying to nail down for a while. A workaround comment without one is basically asking the next engineer to inherit a decision they don't have permission to revisit.
What's worked for me: every workaround block I write now ends with // re-evaluate when X where X is a concrete signal. Usually that's a library version bump, a feature flag flip, or some metric crossing a threshold. I avoid dates here on purpose. They get bumped in review and quietly turn into noise. The behavior shift was that the team started reading comments with a real trigger as load-bearing and the rest as decoration.
The timezone bug story really hit home. Twelve minutes of hotfix turning into four separate duplicated variations and a refactor that took three days is exactly the kind of silent compound interest that makes me nervous every time I see a TODO older than a sprint. I also liked the distinction between a deliberate shortcut with a plan and an unconscious one born from denial. That line about step one becoming the finish line is a painful but accurate summary of how standards erode.
Really appreciate this comment, because you captured the part of technical debt that’s hardest to quantify: the compounding effect.
That “12 minutes now, 3 days later” pattern is exactly what makes these issues so dangerous. The original shortcut rarely looks catastrophic in isolation, but once duplication, assumptions, and copy-paste variations start spreading, the cleanup cost grows exponentially.
I also love how you framed the difference between deliberate and unconscious shortcuts. That distinction matters a lot to me. A conscious trade-off with visibility and a remediation plan is engineering. An unconscious shortcut that quietly becomes “the way we do things” is where teams start losing velocity without realizing why.
And yes, “step one becoming the finish line” is probably the most common failure mode I’ve seen in real projects. Not because teams are careless, but because pressure changes incentives. Once the immediate fire is gone, the system naturally pulls attention toward the next delivery instead of the cleanup.
Really thoughtful takeaway here, thank you for adding it.
Great read, @gavincettolo! Your point about quick fixes creating "invisible drag" hits home.
I've seen teams celebrate a 2-hour hack that turns into months of refactoring hell. In my experience at a fintech startup, we once patched a legacy auth system with duct tape to hit a deadline. Six months later, it was costing us 30% more dev time weekly. What's your go-to metric for spotting when "fast now" becomes "slow forever"?
Spot on, @paolozero.
Fintech is brutal for this stuff! My top metric is cycle time drift: track how long features take from idea to deploy. If simple tasks balloon from days to weeks, technical debt is the culprit. We use DORA metrics (deploy frequency, lead time) at my shop to quantify it.
Yours sounds like a classic "auth debt trap." How'd you finally untangle it? Refactor sprint or big rewrite?
Cycle time drift—stealing that for our next retro! We did a hybrid: carved out a "debt weekend" (48 hours, pizza-fueled) to modularize the auth mess, then incrementally swapped it with OAuth2 over two sprints. Deploy frequency jumped 40%, and no more 3AM fire drills. But your article nailed the psychology—PMs love quick wins, devs dread the bill. Ever tried "debt budgets" like financial ones? Allocate X% of sprint capacity to payoffs?
"Debt weekend" is genius, might pitch that here!
Debt budgets are gold; we cap it at 20% per sprint, tied to OKRs. Forces trade-offs upfront.
Love the OAuth pivot too, pragmatic wins over purist rewrites.
Quick question: According to you, do cultural factors amplify quick-fix temptations, or is it universal dev pain? 😄
I think that quick fixes are global, blame tight budgets everywhere! Debt budgets convinced our PMs; now they forecast "interest payments" in roadmaps. Your article should be required reading for product folks.
What's one fix you've seen backfire spectacularly?
Fair, budgets unite us all!
Worst backfire: a team "fixed" a DB query perf issue by duplicating data across micros. Felt speedy... until schema drift caused week-long outages. Cost: 2 engineers full-time for a month.
Lesson: measure systemic speed, not local.
Paolo, this chat's threading gold! Thank you!
"15-20% of each sprint for technical debt reduction" - that seems like quite a big number. In a typical Scrum team (say, 6 actively working devs), it's:
6 people x 10 days x 8 hours = 480 human/hours * 20% = 96 human/hours, if I count correctly. A lot of work can be done in 96 h/h. Though most teams really have these numbers, I'd say it's still a problem to solve (in several years, the summary amount of waste becomes terrifying). With Kaizen, we reduced debt to 6-7%, and we are still displeased 😡So, I'd say 5% tech debt time should be considered as the goal (especially, in long-term projects, where wasted time accummulates).But on all the other items I agree 👌🏻
That’s a very fair point and, honestly, I’d love to see more teams operating in the 5–7% range instead of 15–20%.
The number in the article comes mostly from observing teams already carrying significant accumulated debt, where some dedicated recovery capacity is needed just to stop the slowdown from compounding sprint after sprint.
I really like your Kaizen example because it highlights the ideal state: technical debt management becomes continuous instead of periodic “cleanup mode”.
In my experience:
The bigger issue is usually not the exact percentage, but whether teams intentionally budget for sustainability at all, many still operate at effectively 0% until delivery speed collapses.
Thanks for bringing real numbers into the discussion
Sharp, honest take. Calling out “it works” as hidden tech debt is dead‑on, and the 1‑3‑10 rule makes the cost impossible to ignore. The cultural angle is even stronger - teams don’t fall apart overnight; they erode through repeated “just this once” shortcuts. Treating quick fixes as visible debt instead of invisible magic is exactly what keeps long‑term velocity intact.
Really appreciate this, especially the point about erosion happening gradually rather than through a single catastrophic decision.
That’s the part I think many teams underestimate: most engineering slowdowns don’t come from one terrible architectural choice, but from hundreds of small “just this once” compromises accumulating over time.
And yes, visibility is the key difference. Technical debt itself isn’t inherently bad, hidden debt is.
Once teams acknowledge quick fixes as conscious trade-offs instead of pretending they’re permanent solutions, they can manage them intentionally rather than letting them silently shape the system.
Glad the 1-3-10 framing resonated 🙌
I’d genuinely love to hear your thoughts on this.
Do you agree with the idea that “quick fixes” often slow teams down long-term, or do you think speed sometimes matters more than clean architecture?
Curious to hear different perspectives 🙂
Quick fix will have cost! So, for that emergency period, it needs to be applied.
But, the moment it gets fixed, then we should take our time to think about the problem and the fix we did and solve it properly!
But, I think, at the least, for the person who did the quick fix and saved the day, can feel to be the hero for that day! 😃
Absolutely, quick fixes are sometimes the right call when the house is on fire 😄
The danger starts when the emergency solution quietly becomes the permanent architecture. That’s usually how teams accumulate invisible complexity over time.
And yes, the person who ships the quick fix does deserve the “hero of the day” moment 🎉 as long as the team also makes space afterward for the less glamorous work of turning that rescue into a sustainable solution. The real win is not just saving production today, but preventing the same fire tomorrow.
True!
Great read, @gavincettolo! Your point about quick fixes creating "technical debt snowballs" really hit home. I've seen teams celebrate short-term wins, only to spend months untangling the mess later. What's one cultural change you've seen that actually prevents this debt from piling up?
Thank you @elenchen, glad it resonated!
Spot on about the celebration trap. The best cultural shift I've witnessed is "debt spikes": short, dedicated sprints (like 1-2 weeks every quarter) where teams exclusively pay down tech debt, no new features allowed. It builds in accountability without killing momentum. Have you tried something similar, or what's worked in your experience?
Love the "debt spikes" idea—feels like a pressure valve! In my last role, we adopted "refactor Fridays," but it fizzled because leadership tied bonuses to feature velocity. We switched to metrics blending speed and debt reduction (e.g., tracking cycle time + code churn). Velocity actually improved 20% long-term. How do you convince skeptical stakeholders that investing in cleanup isn't "wasted time"?
@elenchen Refactor Fridays" scaling to measured outcomes is gold, those blended metrics are key.
For stakeholders, I use a simple ROI frame: "Every hour on quick fixes costs 3x in future maintenance" (backed by our data). Pair it with a quick demo of a refactored module running 2x faster. Turns skeptics into advocates fast. What's your go-to demo or story for winning them over?
My killer story? A legacy API we "fixed" with hacks—deployments took 4 hours. We refactored it in a debt spike, dropping to 10 minutes. Showed execs the demo live, with before/after logs. They greenlit quarterly spikes on the spot! Quick question: How do you prioritize what debt to tackle first when everything feels urgent?
That's a demo masterclass, live proof crushes doubt every time!
Prioritization: We score debt by "pain multiplier" (frequency of touch slowdown impact team frustration).
High scores first.
Tools like SonarQube or custom spreadsheets make it data-driven, not democratic debates. Cuts through urgency noise. Ever used a scoring system, or what's your triage method?
"Pain multiplier" is brilliant, stealing that for my next retrospective! We triage via a heatmap in Jira: red for hotspots blocking >2 devs weekly. Paired with pair-programming sessions on hot debt items—turns cleanup into knowledge-sharing wins. Result? Fewer bugs, happier teams. Final thought: Any books or talks that shaped your views on this?
It is hard to suggest some books, maybe "Working Effectively with Legacy Code" by Feathers for tactics, and Ward Cunningham's tech debt metaphor talk.
Also, "Accelerate" by Forsgren for the metrics backbone.
Thanks for the great thread, @elenchen.
This is the tech-debt argument applied perfectly to the AI era. "It works" is the most dangerous phrase in software because it's where most people stop - and AI makes reaching "it works" so fast that the temptation to stop there is stronger than ever. The quick fix isn't free; you're borrowing speed from your future self at a brutal interest rate.
The AI twist worth naming: agents are exceptionally good at producing "it works" code and indifferent to whether it's maintainable, because they optimize for passing the immediate check, not for the next engineer who touches it. So the discipline of "works AND is clean" now has to be imposed by you, deliberately, because the tool won't do it for free. The faster the generation, the more intentional you have to be about not stopping at "it works." Sharp, evergreen point.
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