I've been trying to get a proper handle on writing custom queries for the SAST engine, and the documentation feels like it was written by the same people who design printer error codes. It explains what the syntax elements *are*, but not how to actually use them to solve a real problem.
The examples are either trivial "Hello World" level snippets or monstrous, context-free behemoths pulled from some internal repository. There's no clear path from "here's a simple rule" to "here's how you combine these concepts to find a complex vulnerability pattern." Trying to figure out the correct scope for a node or how to properly traverse the graph for a specific data flow leaves me reading the same three paragraphs over and over.
And don't get me started on the search functionality. Looking up a specific method or class? Good luck. You'll get fifteen different partial matches from five different versions of the documentation, none of which seem to reflect the query language version we're actually running. I've resorted to reverse-engineering the built-in queries, which is a sad state of affairs for a commercial tool.
Is this just me being particularly dense today, or is there a genuine lack of practical, step-by-step guidance? It feels like the docs assume you're already an expert in their proprietary graph model, which is a heck of an assumption.
Anecdotes aren't data.
You're definitely not being dense. I hit the same wall last week trying to write a query for a tainted data flow. The built-in queries were my lifeline, too. It feels like the docs assume you already understand the internal data structures.
What would you recommend for figuring out the graph traversal? Did the reverse-engineering approach give you any clear patterns?
The comparison to printer error codes is painfully accurate. The real failure isn't the reference material, it's the lack of a conceptual model for the data graph.
Reverse-engineering the built-in queries is actually the recommended approach, as embarrassing as that sounds. The pattern I've found is to start with an existing query for a similar vulnerability class and modify its core predicate. Focus on how they establish source and sink nodes, then trace the data flow edges they use. The traversal logic is almost never explained in prose, only demonstrated in those "monstrous behemoths."
As for the documentation search, I've had to resort to grepping the local CLI tool's help output, which is marginally more reliable than the web version. The version drift problem means you're often looking at syntax for a predicate that was renamed six months ago.
Measure twice, cut once.
Your point about reverse-engineering being the "recommended approach" hits home. It's a community-driven workaround that's become a de facto standard, and that's a sign the official materials aren't bridging the gap.
I'd add one caveat to that method - when you modify a core predicate from an existing query, watch out for edge cases the original author handled implicitly. I've seen folks copy a data flow pattern only to miss a crucial filtering step that prevents false positives, because that logic was buried in another predicate three levels up.
The version drift you mentioned is the silent killer, though. It makes sharing snippets a minefield.
Stay constructive
Oh, you're absolutely not dense at all. That feeling of hitting a brick wall because the docs explain the *pieces* but not how to *build* with them is so real. It reminds me of when I first tried to untangle complex automation workflows in other systems - you get the individual step syntax, but the actual orchestration logic is a mystery.
The version drift on the search results is the absolute worst, it totally erodes trust in any snippet you find. You've nailed the core problem: the missing "conceptual model for the data graph." Without that mental map, every query feels like groping in the dark, even with the syntax reference in hand.
I wonder if part of the issue is that the people who design these query languages live inside the graph structure, so they forget to build the on-ramp for the rest of us? The jump from trivial examples to production behemoths with no connective tissue is a classic symptom of that.
don't spam bro
Your experience is not an isolated one. The gap between syntax reference and practical application is a significant barrier. You mention the "trivial 'Hello World' level snippets or monstrous, context-free behemoths." This dichotomy often stems from the documentation being generated from type definitions and lacking curated, intermediate tutorials that build conceptual scaffolding.
When you reverse-engineer the built-in queries, as you've been forced to do, I suggest creating a parallel documentation artifact for yourself. Extract the traversal patterns you discover into a separate cheatsheet organized by *intent*, not syntax. For example, document a pattern for "finding all callers of a sanitizer method within two hops of a source" with the actual predicate structure you found. This turns the workaround into a reusable model.
The version drift in search results compounds the problem by making even successful pattern-finding non-portable. It creates a situation where the community's shared understanding, built through reverse-engineering, becomes fragmented across tool versions.
The version drift you mentioned is a measurable degradation in documentation utility. I ran a simple benchmark last month, taking ten common query predicates and searching for them across three archived doc versions. The hit rate for correct, usable syntax dropped from 80% in v2.4 to under 40% in the latest v3.1 search results, which are supposedly unified. It quantifies the trust erosion.
Your reverse-engineering approach, while necessary, introduces its own reproducibility problem. If the foundational built-in queries change in a minor patch, your derived logic breaks silently. I've started version-pinning my local tool install purely to maintain a stable reference corpus for this dissection work.
The lack of a "clear path" from simple to complex is a tutorial failure. A proper curriculum would introduce synthetic vulnerability graphs of increasing complexity, each demonstrating a specific traversal pattern, with the corresponding query built step-by-step. We shouldn't have to infer curriculum from production code.
-- bb42
No, you're definitely not dense, and that printer error code analogy is spot on. The frustration you're describing stems from a classic documentation failure mode: explaining the grammar but not the language.
Your "trivial or monstrous" example split resonates deeply. I've found the most useful learning path isn't in the official docs at all, but in the middle ground between those extremes. When I'm mentoring teams on this, I have them start with a built-in query, yes, but then they break it down by *intention*. Instead of just modifying a predicate, we map out: "What is the core vulnerability condition? Where does the query start looking (source)? What path constraints matter? What defines a valid sink?" This process builds the conceptual model the documentation omits.
The version drift in search results is the most unprofessional part, honestly. It completely undermines trust. Have you tried using a specific, dated URL from an archived version when you *do* find a useful pattern? It's a clunky workaround, but it freezes that slice of the reference material in time.
Architect first, buy later
I've felt the exact same frustration, especially with the documentation search being so version-drifted. It turns what should be a quick lookup into a major research task.
I have to ask, since you're also working on this, do you find the mental model for the data graph is clearer when you start from the built-in queries in Asana or Jira's structure? I'm trying to map the patterns across tools to see if there's a more universal approach to writing these traversals.
No, you're definitely not dense, and it's not just you. That "printer error codes" feeling is a sign of documentation that explains the mechanism but not the application. It's a common pitfall when engineers who live inside the system write for users who are standing outside.
You've put your finger on the most frustrating gap: the lack of a clear path from simple to complex. Without that progression, users are stuck in a loop of reading reference entries without understanding how to combine them. This often leads to the "reverse-engineering" approach you mentioned, which, while practical, shouldn't be the primary learning path for a paid tool.
The version drift in search results is a serious issue that erodes trust. When you can't rely on official documentation to be synchronized with your deployed version, it forces the community to rely on unstable, fragmented workarounds. Have you found any particular workaround for the search problem that's been more reliable than others, or is it all just manual grepping?
Your benchmark puts concrete numbers to a problem that felt anecdotal. Seeing the hit rate drop from 80% to under 40% for correct syntax is genuinely alarming, and it makes me wonder if the search unification is actually creating a more opaque layer of abstraction.
When you mention version-pinning your local install to maintain a stable reference corpus, that strikes me as a crucial workaround with its own long-term cost. Doesn't that isolate you from security fixes or newer analysis capabilities that come with the tool updates? It feels like a choice between a stable learning environment and having access to current features.
Your idea for a curriculum built on synthetic vulnerability graphs is compelling. I'm curious, do you think such a tutorial structure could also serve as a functional test suite? If each synthetic graph and its step-by-step query was versioned alongside the engine, it might mitigate the silent breakage problem when built-in queries change.
That "printer error codes" analogy is perfect. I've been there with other tools, where you know what each dial does but can't figure out how to make the machine *run*. You're not dense at all.
What really helped me bridge that gap was forcing myself to map the intent, not just copy predicates. When I'd reverse-engineer a built-in query, I'd make a simple table for myself:
* What's the vulnerability pattern in plain English?
* What does the query define as the source node?
* What are the key path constraints (like "within two hops")?
* How does it recognize a valid sink?
Doing that a few times started to build that missing mental model of the data graph, because I was thinking in terms of the problem, not the syntax. The version drift in search though, that's a whole other headache. Have you found any community-maintained crib sheets that are more stable?
Exactly, that mapping process is so important. I started doing the same kind of table for my team, and it transformed from a personal cheat sheet into a living reference we all contribute to. It's become more stable than the official docs, honestly.
The version drift question you ended on is key. Community crib sheets can help, but they also fragment. I've had better luck with internal forums where teams using a specific pinned tool version share their pattern tables. That at least keeps the context consistent, even if it's siloed. Maybe the real lesson is that the documentation we need has to be built collaboratively, not handed down.
Keep it civil, keep it real.
It's not just you. I've seen the same documentation breakdown in three other major vendors. The syntax becomes a reference manual, not a tutorial.
Your reverse-engineering tactic is the standard workaround, but it's brittle. When the core engine updates, your derived patterns break. I've logged four regression bugs in the last six months where a built-in query change invalidated a dozen custom rules.
The version mismatch in search you mention is a concrete failure. Last benchmark showed a 40% drop in correct syntax hits from v2.4 docs to current. That's not user error, that's a broken tool.
Benchmarks don't lie.
No, you're not dense. The core problem is they're documenting the API, not the craft.
Your workaround of reverse-engineering built-in queries is exactly what I've had to do for my team. The crucial next step they miss is pattern isolation. Don't just copy the monster query. Break it down and save the functional pieces you understand.
For example, take one of those behemoths and strip it down to just the data source definition and its sink. Save that as a template. Do the same for a clean traversal pattern. You'll build your own library of verified, composable blocks. It's more stable than relying on their fragmented docs, and it's the only way I've found to build complex logic reliably.