A Guide to defect Density:
Test Metrics are tricky. They are the only way to measure, yet the variety is overwhelming.
You could be collecting something that isn’t giving you the analytics you want. The safest way here is to walk on the well-beaten path.
Almost every team in the world relies on defect Density to understand defect trends.
Today’s article is an all-in-one guide on Defect Density (DD).
What You Will Learn:
Let’s look at what density literally means.
It is “the degree of compactness of a substance (Source: Google)”.
So, Defect Density is the compactness of defects in the application. (Ok, so it is just a refined version of defect distribution.)
Applications are divided into functional areas or more technically KLOC (Thousand Lines of code). Thus, the average number of defects in a section or per KLOC of a software application is bug density.
It is a simple math.
Step #1: Collect the raw material: You are going to need the total no. of defects (for a release/build/cycle).
Step #2: Calculate the average no. of defects/Functional area or KLOC
Defect density Formula with calculation example:
Example #1: For a particular test cycle there are 30 defects in 5 modules (or components). The density would be:
Total no. of defects/Total no. of modules = 30/5 = 6. DD per module is 6.
Example #2: A different perspective would be, say, there are 30 defects for 15KLOC. It would then be:
Total no. of defects/KLOC = 30/15 = 0.5 = Density is 1 Defect for every 2 KLOC.
Example 2 is just for those teams who are aware of the KLOC and who needs a measurement against it. Most teams don’t work with that kind of a statistic. But if you need to, you can find out how many KLOC your application is.
Every metric that test team collects conveys one of the following:
If not, you are wasting your time.
DD is the most effective way to understand Quality.
For example: An application with DD 5 per KLOC is of better quality vs. another one with 15 per KLOC.
The higher bug density, the poorer the Quality.
It serves two important purposes:
#1) Don’t take into account duplicates/returned defects
Inaccurately computed Defect Density can mislead your team.
Do not include duplicates/returned defects (not a bug, working as intended, not reproducible, etc.) It increases the count of the total no. of defects, which means the DD will increase proportionally. As a result, your defect metric will suggest poor quality, which would be a definite false alarm.
#2) Don’t do this based on one day’s data
Let’s look at this hypothetical situation:
On day 1, the DD is higher. This could send your team into a panic mode immediately.
So, wait till you have better raw material. In other words, a few days’ worth of data.
Also, when computing DD, you want a cumulative defect count.
In the above table, your DD from Day 2 on does not take into account the number of defects so far. It looks at just that day’s data alone.
It is giving me the impression that: “The defect density from day 2 is reducing and increasing and there is no trend.” Also, how can defect density reduce when nothing is done about the defects reported on the day before? Isn’t it? Think about it.
A better way to do this is:
Once again, if doing this daily, take a cumulative defect count into account.
Depending on the level of refinement your team needs, you can tweak this defect metric.
Total no. of High/Critical defects per KLOC or modules
Defect Density Industry Standard:
Well, this varies for every industry, application and every team. Manufacturing would have a specific threshold and it would be completely different for IT.
DD at its face value shows poor quality. But it is, in turn, the seriousness of the individual defects that decide if the product is fit for use or not.
High DD is your indicator to delve deeper and analyze your defects for their consequences.
Who would not like zero defect density, right? Therefore, even though there is no specific standard, the lower this value, the better.
Defect Density is a key quality indicator. You can’t go wrong with collecting and presenting this defect metric. What’s more? It is one of the easiest to compute.
I hope this article has given you enough exposure to start using Defect Density for deeper insights.
Author: STH team member Swati has written this detailed tutorial.
Do you calculate defect density in your teams? If yes, do you do it per cycle, per module or per KLOC? If not, what other metrics help you understand quality? Please share your comments and questions below.