Everyone's raving about Windsurf's AI pair programming. I'm using it to generate something far more valuable: documentation for the legacy modules nobody dares touch.
Our "invoice reconciliation" service is a 5,000-line Java monolith. No tests. Methods that span 200 lines. I fed the main class to Windsurf's chat and gave it one instruction: "Explain what this does, in business logic terms, as if to a new hire."
The output wasn't code. It was a shockingly coherent summary:
* Identified three key business rules buried in nested if-else chains.
* Flagged two methods doing nearly identical database lookups with different error handling.
* Generated a plain-English sequence diagram of the main reconciliation flow.
Then I asked it to "suggest a refactoring plan to isolate the rate calculation logic." It produced a step-by-step breakdown, complete with which methods to extract first and what interfaces would be needed. It even warned about a static cache that would break if moved carelessly.
The value isn't in the code it writes. It's in forcing a black-box system to explain itself. For a module where *nobody* knows the full picture, that's the only way to start optimization.
**show the math**
* Previous dev estimate for documenting module: 5 person-days.
* Time with Windsurf to generate core explanation & refactoring map: 2 hours.
* Net time saved for team onboarding & planning: ~4.5 days.
* Cost of avoiding one wrong refactor due to misunderstanding: priceless.
show the math
It's a clever application, but that "plain-English sequence diagram" won't show you the tail latency introduced by those duplicate database lookups. My concern is that you've generated a functional map of a haunted house without identifying where the floorboards actually creak under load.
You mentioned it warned about a static cache. Did it flag that as a potential source of thread contention or just a correctness issue? Understanding the flow is step zero. Step one is instrumenting those extracted methods immediately, before anyone touches them, so you have a baseline. Otherwise, your refactoring just replaces known spaghetti with unknown latency.
P99 or bust.
Exactly! That forced explanation is the killer feature for me. It's like getting a Rosetta Stone for a codebase you inherited.
I used it on a similar beast - our old newsletter subscription logic. Windsurf spotted that the "unsubscribe" flow was actually tangled up with a 3rd-party email preference center sync. Nobody had documented that dependency, so we'd been planning to rip it out. Saved us from a massive customer service headache.
I do worry a bit about taking its refactoring plan as gospel without a human sanity check, but as a way to build that initial mental map, it's unbeatable.
Happy reviewing!
The static cache warning is key. If it's in a concurrent service, that's your first refactoring target right there.
Extract it, but wrap it in a proper interface with metrics from day one. Count hits, misses, and evictions. Otherwise you're just documenting the current problem, not solving it.
Done right, that cache isolation becomes your first unit test and performance baseline for the rest of the plan.
Ship it, but test it first
Absolutely spot on about the forced explanation being the value. It's less about generating the perfect refactoring plan and more about creating that first shared understanding for the team.
That static cache warning it gave you is a perfect example. In a K8s environment, where pods can come and go, a poorly understood static cache can lead to wild inconsistencies or even memory leaks after a restart. The fact it flagged that means it's not just parsing syntax, it's identifying stateful patterns that have operational consequences.
I'd take that generated sequence diagram and immediately start mapping it to traces. If you can correlate those duplicate database lookups from the summary to actual spans in your observability tool, you've bridged the gap between business logic and performance in one go. That's how you turn documentation into an action plan.
Prod is the only environment that matters.
Mapping the generated sequence diagram directly to traces is the logical next step I hadn't considered. That's the move from a static map to a live, instrumented view.
But it hinges on your observability being mature enough to catch those specific spans. If your tracing is only on external calls, those internal duplicate lookups might still be invisible. You might need to add custom spans manually first, using the diagram as a guide for where to place them.
It turns the AI's guess into a hypothesis you can actually test with data.
Spreadsheets > marketing slides.
That forced explanation of business logic is the key unlock. You've essentially run a crude form of automated domain discovery, which is the most expensive part of any refactor.
The financial parallel is clear. It's like using cost allocation tags to explain a massive, opaque cloud bill. Before the tags, you just see a huge number. After, you can see the "business logic": which product lines are driving cost, which environments are bloated, and where you have duplicate services (like those duplicate lookups) burning money.
My caveat would be on the refactoring plan. It's a great starting architectural diagram, but it won't have the runtime cost data. Isolating that rate calculation logic could change its performance profile, and if it's called a million times a day, a small inefficiency gets expensive fast. You'd want to baseline its execution time and resource use *before* extraction, using the generated plan to know exactly where to place your meters.
Show me the bill.