MICs on technical debt can be unpredictable

Last updated on June 17th, 2021 at 02:00 pm

Few senior management teams would seriously consider making decisions about financial instruments without carefully estimating their effects on revenue and expenses. Most enterprises support decision-makers with an impressive array of tools, historical data, and skilled financial professionals. Yet few organizations invest at similar levels to support estimators of the MICs involved in undertaking engineering efforts. A similar dearth of resources affects those who estimate the effects on revenue due to carrying technical debt.

A composite satellite view of Antarctica
A composite satellite view of Antarctica. Composite by Dave Pape using NASA’s Blue Marble data set. Exploring unknown territory, as Roald Amundsen did in 1911-12, is far more difficult and riskier than exploring mapped territory. For this reason, managing technical debt is more successful when we have even minimal capability for estimating the MICs of carrying technical debt. Courtesy Wikimedia Commons.

A resource shortage of this kind can have starkly negative effects. The inherent difficulties of projecting the effects of both carrying and retiring technical debt create uncertainties in project budgets and schedules.

MICs can fluctuate dramatically depending on a range of factors. These factors include:

  • The kind of work underway on the asset that carries the debt
  • How the debt affects customers and what they’re doing at any given time
  • The difficulty of researching engineering problems arising from the debt
  • The loss of revenue due to debt-related delays in reaching the market
  • A loss of sales due to semi-catastrophic failures in customer demonstrations

In short, MICs are often unpredictable [Allman 2012].

The state of the art

Most of the research into the effects of carrying or retiring technical debt has focused on engineering activity, and specifically, software engineering activity [MacCormack 2016] [Kamei 2016]. By comparison, research has been less intensive for effects on other activities—marketing, sales, regulatory compliance, to name a few. And in many cases, the effects of technical debt on these other activities are the most significant.

Consider first the effect of technical debt on enterprise expenses. The kind of maintenance and enhancement work performed on a set of assets bearing technical debt can determine the depressive effect on productivity. And declines in productivity directly affect MICs. In many cases, projecting future MICs associated with any given class of technical debt can be difficult. The difficulty arises because we might not know with sufficient certainty what projects will be active in the intermediate term or long term future, and what kind of work those projects will undertake. Even when we do know these things, the level of involvement with instances of particular classes of technical debt can be difficult to project enough certainty to be useful.

Turning to revenue, for most organizations, the picture is also bleak. Because we can’t retire some classes of technical debt incrementally, retirement projects can have significant impact on operations and revenue. Research in this area is even more limited than in the area of effects on productivity.

Last words

Projecting MICs with useful accuracy would be a valuable capability. Making MICs more predictable would require systematically gathering data and building expertise for projecting MICs for your enterprise. That problem is more tractable than the more general problem of projecting MICs absent specific knowledge of enterprise characteristics.

An enterprise-specific MICs projection capability could elevate the quality of decisions regarding resource allocation for projects of all kinds, including technical debt retirement projects. Policymakers can play an important advocacy role in establishing such a capability.

References

[Allman 2012] Eric Allman. “Managing Technical Debt: Shortcuts that save money and time today can cost you down the road,” ACM Queue, 10:3, March 23, 2012.

Available: here; Retrieved: March 16, 2017

Cited in:

[Kamei 2016] Yasutaka Kamei, Everton Maldonado, Emad Shihab, and Naoyasu Ubayashi. “Using Analytics to Quantify the Interest of Self-Admitted Technical Debt,” 1st International Workshop on Technical Debt Analytics (TDA 2016), 68-71.

Available: here; Retrieved: November 28, 2017

Cited in:

[MacCormack 2016] Alan MacCormack and Daniel J. Sturtevant. “Technical debt and system architecture: The impact of coupling on defect-related activity,” The Journal of Systems and Software 120, 170–182, 2016.

Available: here; Retrieved: November 19, 2017.

Cited in:

Related posts

MICs can fluctuate dramatically

Last updated on June 16th, 2021 at 04:28 pm

A common assumption vis-à-vis technical debt is that we can model its productivity-depressing and velocity-reducing effects. We model them as the “interest” on the technical debt (MICs). And we assume that MICs are relatively constant over time. In practice, MICs can fluctuate dramatically. Those fluctuations provide planners valuable insight and flexibility, if they choose to use it. Unfortunately, most plans I have seen make the assumption that MICs are relatively stable.

An example of MICs behavior

30-year average fixed mortgage rates in the United States, 2012-2017
30-year average fixed mortgage rates in the United States, 2012-2017, in %. Over this five-year period, rates did fluctuate. But they did so in a narrow range of from 3.3% to just over 4.5%. When we speak of “interest,” we evoke an impression of relative stability. This happens even when we’re speaking of technical debt. MICs for technical debt can vary from 0 to well above MPrin in any given time period. That’s one thing that makes the term “interest” so misleading in the context of technical debt. Data provided by U.S. Federal Reserve Bank of St. Louis [Federal Reserve 2017].

As an example of this assumption is available in a paper by Buschmann [Buschmann 2011b]. He states that the longer we wait to retire technical debt in design and code, the larger the amount of interest. This presumes constant or non-negative MICs. That assumption that might be valid for some situations, but it isn’t universally applicable.

Consider a project that entails maintenance or extension of parts of the system that don’t manifest a specific class of technical debt. And suppose that the assets in question don’t depend on elements that do manifest that debt. Such a project is less likely to incur the MICs associated with that debt. So with respect to any particular class of technical debt, there might be time periods in which no projects incur MICs. During those periods, the interest accrued can be zero. In other time periods, the interest accrued on account of that same class of technical debt could be very high indeed.

These effects are quite apart from the tendency of MPrin to grow with time, as we noted in an earlier post (see “Debt contagion: how technical debt can create more technical debt”).

Last words

A capacity for projecting MICs associated with a particular class of technical debt can be useful to planners as they work out schedules for maintenance projects, development projects, and technical debt retirement projects. Technical debt retirement projects are also subject to MICs, including from classes of technical debt other than the debt they’re retiring.

Analogous to the functioning of governance boards, a technical debt resources board could provide resources for evaluating assessments of likely MICs for maintenance projects, development projects, and technical debt retirement projects. Decision makers could use these assessments when they set priorities for these various efforts. I’ll say more about technical debt resources boards in future posts.

References

[Allman 2012] Eric Allman. “Managing Technical Debt: Shortcuts that save money and time today can cost you down the road,” ACM Queue, 10:3, March 23, 2012.

Available: here; Retrieved: March 16, 2017

Cited in:

[Buschmann 2011b] Frank Buschmann. “To Pay or Not to Pay Technical Debt,” IEEE Software, November/December 2011, 29-31.

Available: here; Retrieved: March 16, 2017.

Cited in:

[Federal Reserve 2017] Federal Reserve Bank of St. Louis. “30-Year Fixed Rate Mortgage Average in the United States (MORTGAGE30US).” Weekly time series.

Available: here; Retrieved: November 25, 2017.

Cited in:

[Kamei 2016] Yasutaka Kamei, Everton Maldonado, Emad Shihab, and Naoyasu Ubayashi. “Using Analytics to Quantify the Interest of Self-Admitted Technical Debt,” 1st International Workshop on Technical Debt Analytics (TDA 2016), 68-71.

Available: here; Retrieved: November 28, 2017

Cited in:

[MacCormack 2016] Alan MacCormack and Daniel J. Sturtevant. “Technical debt and system architecture: The impact of coupling on defect-related activity,” The Journal of Systems and Software 120, 170–182, 2016.

Available: here; Retrieved: November 19, 2017.

Cited in:

Related posts

The concept of MICs

Last updated on June 16th, 2021 at 03:13 pm

Using the term interest to refer to the metaphorical interest charges of a technical debt is risky. The risk arises from confusing the properties of financial interest with the properties of the metaphorical interest charges on technical debt. Using an alternative term that makes the metaphor obvious can limit this risk. One such term is metaphorical interest charges, or for convenience, MICs.

What exactly are “metaphorical interest charges?”

Loose change
Loose change. The MICs on technical debt accumulate in two ways: (a) as “loose change,” namely, small bits of lost time, delays, and depressed productivity; and (b) as major blows to enterprise vitality in the form of lost revenue, delayed revenue, and missed market opportunities. Hard to say which category does more damage.

MICs aren’t interest charges in the financial sense. Rather, the MICs of a technical debt represent the total of reduced revenue, incidental opportunity costs, and increased costs of all kinds resulting from carrying that technical debt. (Actually, now that I think of it, MICs can include financial interest charges if we find it necessary to borrow money as a consequence of carrying technical debt.) Because the properties of MICs are very different from the properties of financial interest charges, we use the term MICs to avoid confusion with the term interest from the realm of finance.

Briefly, MICs are variable and often unpredictable [Allman 2012]. MICs differ from interest charges on financial debt for at least eight reasons. For any particular class of technical debt:

I examine each of these properties in more detail in the posts listed above.

References

[Allman 2012] Eric Allman. “Managing Technical Debt: Shortcuts that save money and time today can cost you down the road,” ACM Queue, 10:3, March 23, 2012.

Available: here; Retrieved: March 16, 2017

Cited in:

[Buschmann 2011b] Frank Buschmann. “To Pay or Not to Pay Technical Debt,” IEEE Software, November/December 2011, 29-31.

Available: here; Retrieved: March 16, 2017.

Cited in:

[Federal Reserve 2017] Federal Reserve Bank of St. Louis. “30-Year Fixed Rate Mortgage Average in the United States (MORTGAGE30US).” Weekly time series.

Available: here; Retrieved: November 25, 2017.

Cited in:

[Kamei 2016] Yasutaka Kamei, Everton Maldonado, Emad Shihab, and Naoyasu Ubayashi. “Using Analytics to Quantify the Interest of Self-Admitted Technical Debt,” 1st International Workshop on Technical Debt Analytics (TDA 2016), 68-71.

Available: here; Retrieved: November 28, 2017

Cited in:

[MacCormack 2016] Alan MacCormack and Daniel J. Sturtevant. “Technical debt and system architecture: The impact of coupling on defect-related activity,” The Journal of Systems and Software 120, 170–182, 2016.

Available: here; Retrieved: November 19, 2017.

Cited in:

Related posts

How financial interest charges differ from interest charges on technical debt

Last updated on June 16th, 2021 at 12:43 pm

Second only to the term debt, the term interest is perhaps the most common financial term in the technical debt literature. In the financial realm, interest charges are the cost of using money. Usually, we express interest charges as a percentage rate per unit time. By contrast, metaphorical interest charges (MICs) on technical debt work differently. Failure to fully appreciate that difference can create problems for organizations as they try to manage their technical debt.

Credit cards also have interest charges
Credit cards. Revolving unsecured charge accounts are perhaps the most familiar form of financial debt. They do have one thing in common with technical debt: with either one, getting into debt over your head is easy.

The notion of interest is deep in our culture. We understand it well. But the way we understand it corresponds to fixed or slowly varying interest rates. This understanding biases our perception of technical debt.

The root of the problem

Because we’re so familiar with financial interest, we perceive the elements of technical debt as imposing a cost that’s a relatively stable fraction, per fiscal period, of the initial MPrin. This belief doesn’t correspond to the reality of technology-based systems, which are the targets of the technical debt metaphor.

MICs on technical debt differ from the interest on financial debt in two ways.

  • MICs depend strongly on whether and how the people of the enterprise interact with the assets bearing the technical debt.
  • The MICs on technical debt include the value of opportunities lost (opportunity costs). These losses are due to depressed productivity and reduced organizational agility.

Neither of these factors has a financial analog. In finance, interest charges depend solely on a mathematical formula involving the interest rate and principal.

Last words

In the next few posts, I’ll explore the properties of metaphorical interest charges. This investigation helps clarify how they differ from financial interest charges. It also clarifies how that difference contributes to difficulties in managing technical debt.

References

[Allman 2012] Eric Allman. “Managing Technical Debt: Shortcuts that save money and time today can cost you down the road,” ACM Queue, 10:3, March 23, 2012.

Available: here; Retrieved: March 16, 2017

Cited in:

[Buschmann 2011b] Frank Buschmann. “To Pay or Not to Pay Technical Debt,” IEEE Software, November/December 2011, 29-31.

Available: here; Retrieved: March 16, 2017.

Cited in:

[Federal Reserve 2017] Federal Reserve Bank of St. Louis. “30-Year Fixed Rate Mortgage Average in the United States (MORTGAGE30US).” Weekly time series.

Available: here; Retrieved: November 25, 2017.

Cited in:

[Kamei 2016] Yasutaka Kamei, Everton Maldonado, Emad Shihab, and Naoyasu Ubayashi. “Using Analytics to Quantify the Interest of Self-Admitted Technical Debt,” 1st International Workshop on Technical Debt Analytics (TDA 2016), 68-71.

Available: here; Retrieved: November 28, 2017

Cited in:

[MacCormack 2016] Alan MacCormack and Daniel J. Sturtevant. “Technical debt and system architecture: The impact of coupling on defect-related activity,” The Journal of Systems and Software 120, 170–182, 2016.

Available: here; Retrieved: November 19, 2017.

Cited in:

Related posts