You may be working in an organization where technical debt is an intentional choice, in exchange for gaining the benefit of time or resources. However, if the debt is hidden, by not collecting metrics, managers can play the "pass the hidden debt" game, hoping to drop it into someone else's hands before interest comes due. Keeping the debt visible helps keep the level of gaming down.

Recognizing Technical Debt
If you’re a tester, how can you recognize technical debt? Look for indicators like these:

• As soon as the developers fix one defect, five more previously unknown defects pop up.
• Testing time increases disproportionately to the development time.
• Your developer to tester ratio is 1:1, and you still don’t have enough testers because you’re sure you haven’t fully tested the product.
• Developers start talking about re-architecting or redesigning the product. If the design debt is overwhelming, the developers start saying things like, "Nope, I’m not touching that. That’s a feature, not a bug. If you want me to fix that, I’ll have to re-architect the product."
• Developers refuse to touch a part of the product saying, "No one but Fred can touch that. I know Fred left three years ago, but no one else can work on it, because we don’t understand it."
• Your developers threaten to leave because all they do is "maintenance."
• Developers and testers become experts at crisis management, spending more time supporting current customers—solving customer problems, fixing defects, verifying the fixes, and answering customer questions—than they spend developing and testing the product scheduled for release next month.
• You’re putting out point releases or patches weekly or daily.
• You can’t decide what not to test, because the risk of not testing everything, even for a point release, is too high.
• You ship the product not because it’s ready, but because everyone is too tired to continue the crunch mode you’ve been in.
• You stop testing because you don't want to find more defects.
• Your cost to fix a defect continues to increase, from release to release. (If you’d like to see a picture of this dynamic, check out Weinberg’s Quality Software Management, Volume 4).

Taking a Closer Look
Are you seeing signs of technical debt? If you suspect you’re floundering in technical debt, take a few measurements to see if you can make sense of the data. The following table shows data I gathered from one project. I looked at the state of the product on a quarterly basis, counting the total number of lines of code. (We used the Unix command, wc –l recursively over all the code in the branch on the specific date.) When taking gross measures, LOC is appropriate because it gave us insight into what was happening in the project. For other measurements, LOC is not appropriate.

Aside from LOC, measure the Fault Feedback Ratio, the FFR. To measure FFR, we took the defect data from the bug-tracking system, and for that date, we measured the number of bad fixes to the total number of fixes. We also determined the cost to fix a defect pre-release.

To measure cost to fix a defect, we tracked all the defects for the release, and took the average cost to fix. See my previous column "What Does It Cost to Fix a Defect?" for more information on how you would calculate the cost to fix a defect.

It’s always tempting to jump to conclusions from looking at the data. Don’t. Probe and ask questions to make sure you know what the data really means. For example, the code size increased almost 50 percent each quarter. Do I think the developers are capable of writing that much high-quality code? What did the developers do, to generate that amount of code? Did they leave experiments (rejected designs) in the code? Did they hire more people? Did they use a code generator? If you’d like more guidance on what you should expect for increases in code size, see Capers Jones’ book, Software Assessments, Benchmarks, and Best Practices.

In this case, it turns out that instead of re-architecting the code to account for new knowledge and changes in requirements, the developers were cutting and pasting code all over the system. The system was bloated, and every time the developers had to make a fix in one place, they had to remember the forty-seven other places to fix.

Then I look at the FFR. Does it make sense? What makes the FFR go up or down? In this case, the FFR started high, and aside from the third quarter, continued to go up. What happened in the third quarter to make the FFR go down? In the third quarter, one development team re-architected one small module that was originally a huge source of defects. With the new version, the total number of bad defects went down and the cost to fix a defect went down. They had paid off some of the "debt," so now they were paying less "interest" on the debt. You may also want to look at FFR by subsystem or module, to see if one subsystem or module is causing much of the defect-fixing pain.

The cost to fix a defect for this organization is extremely high. If the developers and testers find twenty defects a day, then they generate about sixty days’ worth of work for every day of testing.

Let me know your thoughts on technical debt. My next column will provide pointers for diagnosing and decreasing the debt.

Acknowledgements
I thank Esther Derby, Dale Emery, Dave Smith, and Jerry Weinberg for their review on this column.