Last updated on June 17th, 2021 at 07:49 pm
For financial debts, the interest charges associated with a unit of debt are (usually) the same for every unit of debt incurred under the same loan agreement. But for technical debt, the MICs associated with a given instance of a class of technical debt might differ from the MICs associated with any other instance of the same class of technical debt. They can differ even if we incurred those instances of technical debt at the same time. And they can differ even if they formed as results of a single decision or sequence of events. Unlike the transactions on a credit card, the interest charges can vary for instances of the same kind of technical debt.
Why MICs can differ from instance to instance
For most financial debts, a single algorithm determines the interest charges for every unit of a particular class of debt. Following the technical debt metaphor, people tend to assume that the MICs on every instance of a particular class of technical debt are uniform across the entire class.
But in practice, uniformity assumptions with regard to MICs are generally invalid. Given two different instances of the same kind of technical debt, the MICs associated with modifying asset components in and around those two instances can differ significantly. For any given instance of a particular class of technical debt, MICs can depend on whether engineers must interact with that part of the asset. And when they do interact with a given asset component, MICs can also depend upon the transparency and condition of that asset component.
Two examples illustrating varying MICs
For example, an instance of technical debt might reside in a setting that relatively few local experts understand. The people who are capable of doing that work might be in high demand, or heavily committed, or expensive. Subsequent scheduling difficulty can lead to delays or service interruptions associated with completing the required work. That can result in lost revenue, which also contributes to MICs. Meanwhile, instances of the same kind of technical debt residing in other parts of the asset might be addressable by less expert staff. They might be in lesser demand, and less well compensated. Service interruptions might be shorter, and lost revenue less. The MICs associated with these two cases can therefore differ significantly.
As a second example, consider documentation deficits. Suppose an engineer needs documentation to determine how to proceed, and that documentation doesn’t exist. The engineer must then resort to alternatives that might be more time-consuming. He or she might read code or specifications, or interview colleagues. But for two instances of the same kind of technical debt, the need to refer to documentation can differ. The engineer might need documentation for one instance in one part of the asset, but not for another.
Another form of documentation deficit can be especially costly. Suppose engineers need documentation, and it does exist, but it’s out of date or incorrect. Those engineers might make costly, potentially irreversible errors when they undertake maintenance or extension activity. When testing uncovers the defects the engineers unwittingly introduced due to the defective documentation, the damage is less. But if testing doesn’t catch the defects, they might somehow find their way into production. If they do, the revenue or liability impact can be substantial. And the impact can vary from instance to instance of the technical debt in question. These effects are all forms of MICs.
So MICs can vary almost on an instance-by-instance basis. Or they might be constant across instances. It’s difficult to say. But the easy assumption—that MICs are the same for all instances of a given class of technical debt—the easy assumption is probably incorrect.
Available: here; Retrieved: November 29, 2017