Dynamic connascence, the deeper problem most teams miss
Let’s talk about a form of code dependency that doesn’t show itself until your system is live, dynamic connascence. This isn’t something you find statically in code reviews. This is behavior revealed only during runtime, which means your quality assurance and test coverage don’t always catch it. That also means when something breaks, it breaks in production, and by the time you realize it, your customers already have.
Dynamic connascence refers to runtime dependencies between parts of your system. These dependencies are subtle because they depend on how components behave while the application is operating, not just how the code looks when static. It’s invisible to most static analysis tools, so engineers often overlook it until something fails under load, in threaded execution, or during actual usage in production environments.
If you’re a CTO or Chief Product Officer, dynamic connascence should be on your radar. This hidden coupling can introduce performance issues, bugs that are hard to reproduce, and unexpected technical debt. It becomes more dangerous the bigger your codebase gets. The complexity escalates as teams scale and efforts decentralize. Most importantly, it hinders your ability to deploy software rapidly and safely.
Connascence of execution, when order becomes a liability
Execution order shouldn’t dictate functionality, but in some systems, it still does. That’s connascence of execution. When your system breaks because two lines of code were swapped or a process didn’t run in the exact expected sequence, you’re fighting hidden coupling.
This dependency can sneak into platforms that evolve quickly. A developer might add features without realizing that they’ve introduced temporal dependencies. From that point on, any reordering, even by accident, can break functionality. It’s inefficient. It slows down engineering velocity. And it’s everything we don’t want when scaling software.
Now imagine someone reviewing that part of the code in six months who didn’t write it and isn’t aware of the internal order requirement. The failure risk is high. That’s when you see warning comments in code like “DO NOT MOVE THIS LINE” with several exclamation marks. That’s not robust engineering. That’s patchwork survival.
For those running product, ops, or engineering, connascence of execution is more than a code smell, it’s a barrier to platform resilience. If your execution sequence breaks your functionality, you’ve also created a bottleneck for onboarding engineers, refactoring logic, or automating processes with confidence. Fixing it involves enforcing clearer contracts between modules and refactoring logic so that function, not just sequence, drives behavior.
In a high-growth company, quality should scale with speed, not get compromised by it. This is a place to start.
Connascence of timing, the hidden risk in concurrent systems
Connascence of timing shows up when how long something takes, or the exact moment it runs, determines whether your system behaves correctly. This isn’t just about loading speeds or UI performance. This is about correctness. Whether an operation returns the right result or fails entirely depends on something as unstable as thread scheduling or network latency.
These issues happen when operations compete for limited resources at the same time. Think multithreading, shared memory, network API calls under load, any asynchronous or parallel process is a potential source for this kind of problem. The worst part? These bugs don’t show themselves during standard testing. You only catch them under real execution conditions, and even then, not consistently. They surface unpredictably and are difficult to trace after the fact.
If you’re making decisions that impact product stability or system reliability, this deserves attention. Connascence of timing isn’t a rare edge case in modern systems. It’s a structural risk. And unless your engineers are investing in concurrency profiling, race condition testing, and defensive architecture design, you’re exposed. Not just to bugs but to cascading failure points across services.
Teams often focus too much on deployment velocity and not enough on behavior under load. Timing errors compromise your ability to scale, especially in asynchronous communication systems. If your architecture can’t tolerate timing variations, you’re facing fragility at a systemic level. That’s avoidable, with awareness and investment in robust concurrency patterns and observable traces under production-like conditions.
Connascence of value, hard-coding fragility into the system
Connascence of value happens when two or more parts of your system assume the same value must exist, and that assumption is hard-coded. Whether it’s a price, configuration setting, or internal state, when multiple modules rely on magic numbers or shared constants without a single source of truth, your system becomes fragile by design.
The problem isn’t just duplication. It’s that when a value changes, you now need to hunt down and update multiple places. Miss one, and the whole system becomes inconsistent. Over time, this creates instability. Code that looks correct in isolation fails in combination. Your development slows, and operational errors increase.
In the example from the article, both the test case and the implementation assume a fixed price of “50” for a certain item. When that value breaks, the test fails, not because the logic is wrong, but because the test and implementation are over-coupled to the same constant.
For business and technical leaders, this is critical. Hard-coded values reduce adaptability. Your systems can’t respond quickly to change. Market conditions shift, pricing models evolve, and user settings need flexibility. If updates require code changes rather than configuration or data-driven rules, the system you’re scaling will keep resisting you.
The fix is straightforward but requires discipline, centralize source data and inject values where needed. It’s not glamorous work, but it’s what makes continuous delivery actually sustainable. Your platform becomes more modular, more testable, and most importantly, faster to evolve. That compounds over time. And that has impact.
Connascence of identity, when shared references drive instability
Connascence of identity happens when two or more parts of a system must reference the exact same object to function correctly. The moment one piece changes its reference to a new object, the others need to do the same, or the system breaks. This isn’t just state mismatch. It’s structural risk embedded in object relationships.
In the code example, both the sales and inventory reports originally point to the same ReportInfo object. When one of them swaps in a new object but the other doesn’t follow, behavior diverges. This divergence may not cause a crash but it does create logical inconsistency, and that’s usually harder to identify and fix. These errors tend to sneak in during feature additions, especially in mutable systems, and only become obvious after a failure in business logic.
From a leadership perspective, this creates systemic fragility. The risk compounds when systems don’t make these connections explicit. When your architecture silently depends on shared references remaining aligned, you’re carrying invisible dependencies across modules. These dependencies slow down your ability to scale and isolate changes, because the actual impact radius is broader than it looks.
To prevent this, require clarity of ownership over shared entities. Make reference ownership explicit. Architect systems so that synchronization either happens automatically or not at all. Unintentional divergence, across services, across teams, creates trust issues in data. You don’t scale those problems. You shut them down early.
Reduce dynamic connascence to increase system resilience
Dynamic connascence isn’t theoretical. It shows up in production systems and slows down real-world velocity. The more of it you have, the more tightly coupled your components behave at runtime, even if the code looks modular on the surface. Over time, these hidden dependencies act as a drag on innovation and system stability.
The key is naming the problem. Most developers are aware of these friction points, they’re embedded in hard-to-change modules, they’re what break unexpectedly during releases or what need patches before demos. What the concept of connascence does is give you the framework to classify and reduce them methodically. It’s an engineering tool that informs architectural discipline.
If you’re a CEO, CTO, or product executive overseeing systems expected to evolve, formalizing understanding of connascence makes sense. This is how you embed resilience without sacrificing speed. You build competence in your teams to refactor with purpose, to separate concerns, and to move faster without accumulating risk at scale.
The shift is simple: treat coupling not only in code but in runtime behavior. Once you address dynamic connascence directly, across execution, timing, value, and identity, you move closer to continuous deployability, higher code ownership, and systems that adapt across markets and lifecycles. That’s leverage in every direction.
Main highlights
- Prioritize identifying runtime dependencies: Dynamic connascence creates hidden code coupling that only emerges at runtime, leading to late-stage instability. Leadership should encourage engineering teams to expose and reduce these dependencies early through testing, monitoring, and architectural reviews.
- Reduce sequence-based coupling in workflows: Connascence of execution forces systems to rely on strict order of operations, increasing fragility. Leaders should champion process and code refactors that decouple execution logic from sequencing to enable safer change and faster iteration.
- Strengthen resilience to timing variations: Connascence of timing introduces system failures that depend on thread execution, latency, or timeout behavior. Executives should invest in infrastructure and development practices that mitigate race conditions and improve concurrency reliability.
- Eliminate hard-coded values across systems: Connascence of value makes systems brittle by embedding shared constants across code modules. Organizations should standardize on centralized configurations or parameter injection to reduce duplication and minimize unnecessary maintenance risk.
- Make shared object ownership explicit: Connascence of identity causes silent failures when components depend on the same object reference but fall out of sync. Leaders should ensure that teams follow clear patterns for reference management and shared data coordination.
- Drive awareness of hidden architectural debt: Dynamic connascence formalizes unseen runtime coupling that slows delivery and reduces agility. Executives should prioritize frameworks and trainings that help engineering teams surface, diagnose, and resolve these hidden risks to scale safely.
