Product Development & Management

Seven Metrics to Reduce Tech Debt

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Technical debt refers to the implied cost of rework necessitated by choosing a fix that’s easy to implement now, rather than a better resolution that would take longer. In simple terms, tech debt points to the work your team will have to do in the future as a result of cutting corners today. 

That said, tech debt isn’t a negative indicator across the board, and it’s impossible to avoid altogether. Sometimes taking on tech debt makes sense, like when you need to speed your time to market or account for last-minute spec changes.  

Complex software that requires extensive refactoring — that is, restructuring of existing code — is particularly associated with tech debt, but refactoring is not only a response to problems with the code. Refactoring often represents an evolving understanding of a problem and the best way to solve it.  

Still, it behooves teams to minimize unnecessary tech debt. In our ebook, “The Essential Guide to Software Development Team Metrics,” we dig into metrics to assess the speed, accuracy, quality and health of your team. Seven of these metrics are especially important to bear in mind when working to minimize tech debt: 

Ticket churn 

Also referred to as “ticket bounceback,” ticket churn is a measure of how many tickets have been moved back in progress over a period of time. Ticket churn gauges rework, so high ticket churn can indicate increasing tech debt, and is likely to impact your team’s velocity and overall product quality. 

New vs. closed bugs 

Track how many bugs are opened against how many are closed to find the rate at which your team clears bugs. This metric is an indicator of your overall tech debt, as well as whether your team is moving in the right direction in terms of general code quality.  

Bug burndown 

Development and quality assurance teams use bug burndown to understand open bugs and their projected closures based on the average bug closure rate. Teams that don’t keep a close eye on bug burndown can lose a handle on their overall product quality and take on excessive amounts of tech debt in their effort to fix bugs quickly. 

Percentage of high-priority bugs 

This straightforward calculation — dividing the number of current, high-priority bugs by the total number of bugs — is simply the percentage of bugs that your team has tagged as high-priority (sometimes high-severity) due to their impact on customers or the product as a whole. Tracked over time, this metric illuminates part of the story of both product quality and tech debt. An increasing trend of more high-priority bugs is often symptomatic of a team struggling with product requirements, test cases and test suites.  

Code churn 

Code churn reflects the number of lines of code deleted and added in the same line. It’s a measure of activity over time that highlights hotspots in your code.  

With brand-new features, a lot of activity in one area isn’t a problem, but over time, code churn should diminish; if it doesn’t, you’re doing too much rework and accumulating an unnecessary amount of tech debt. High code churn can also predict a drop in quality and speed.  

Code coverage 

Also called test coverage, code coverage is the percentage of lines of code that are executed when running your test suite. Fundamentally, code coverage refers to how effective your test process is at producing a quality product. As a rule of thumb, coverage should be in the 80-90% range.  

Code coverage doesn’t measure the inherent quality of your product; rather, it helps reveal the process your team is undertaking to achieve a quality product. Code coverage highlights breakdowns in your test process, like when new code is added but not tested.  

If your code coverage percentage drops over time, devote more resources to test-driven development (TTD) and make sure untested areas are covered. A high-quality test process heads off quality issues and reduces tech debt, setting your team up to be nimbler in the future.  

Frontend app response time 

Frontend app response time, or the average time it takes for your pages to be available to end-users, helps your team identify when important product updates or infrastructure upgrades are necessary to keep your solutions running smoothly.  

Usually monitored closely by DevOps teams, frontend app response time is critical to product success. Your product might add huge value, but users will start looking elsewhere if it’s too slow. An increase in response time often suggests rising technical debt: your short-term solutions are creating long-term problems for your customers and your team.  

Get the full story on metrics for development teams: Pick up The Essential Guide to Software Development Team Metrics now.

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