All In One Guide On Defect Density – Its Importance and How to Calculate It.

Detailed Manual on Defect Density:

Test Metrics can be daunting. They are a sole method of evaluation, but the range can be overwhelming.

You may be collecting data that fails to give you the insights you need. The best solution here is to stick with the traditional methods.

Reliance on defect density to understand defect patterns is globally recognized by virtually all teams.

This article offers a fully detailed review of defect Density (DD).

Defect Density Defined

Let’s delve into what density means in its actual context.

It signifies “the extent of compactness of a substance (Source: Google)”.

Therefore, Defect Density is tantamount to the concentration of defects in an application. (In other words, it’s an advanced way of defining defect distribution.)

Applications get divided into functional areas or, in technical terms, KLOC (Thousand Lines of Code). Hence, bug density is the mean count of errors in a particular section or per KLOC in a software application.

Bug Density Calculation

The counting methodology is a straightforward process.

Step One: Obtain the necessary dataset: The overall number of defects (for a release/build/cycle) is required.

Step Two: Determine the average count of defects per functional area or KLOC.

The formula for calculating Defect Density with a demonstrated calculation:

Instance #1: In a particular test cycle, there are 30 defects across five modules (or components). The density is:

Total defects divided by total modules = 30/5 = 6. Hence the DD per module is 6.

Instance #2: From a different viewpoint, let’s presume 30 defects for 15KLOC. The computation would be:

Total defects divided by KLOC = 30/15 = 0.5. Hence the Density is 1 defect for every 2 KLOC.

Instance 2 is pertinent for teams that work with KLOC and require measurements based on it. However, the majority of teams do not operate with this kind of data. But if necessary, you can calculate the number of KLOC within your application.

Bug Density Importance

Every statistic gathered by the testing team communicates one of the following:

• Progress
• Efficiency
• Quality

If not, your efforts are in vain.

DD happens to be the most reliable method for comprehending Quality.

For instance: An application with a DD of 5 per KLOC suggests better quality than another app with a DD of 15 per KLOC.

The higher the bug density, the worse the Quality.

It serves two crucial roles:

• Informativeness: Knowledge is invaluable, right? Pinpointing your application’s vulnerable areas aids in determining if it is “fit-for-use”.
• Actionable: High DD modules require attention. DD assists in spotting them.

Things to evade

#1) Excluding duplicates/returned defects

Incorrect computation of Defect Density can mislead your team.

Avoid counting duplicates/returned defects (not a bug, works as planned, non-reproducible, etc.). Integrating them will elevate the defect count, subsequently causing a proportional rise in the DD. As a result, your defect metric would indicate low quality, which would be a false alert.

#2) Don’t depend on a single day’s data

Imagine this hypothetical scenario:

Day 1 experiences a high DD, which could alarm your team immediately.

Therefore, refrain until there’s stronger information – data from several days.

Besides, a total defect count should be taken into consideration when calculating DD.

In the above table, the DD from Day 2 and beyond does not include the defects till that day. It only adheres to the data for each individual day.
This creates an illusion that: “The defect density from day 2 seems inconsistent, with no apparent pattern.” It also builds a question about how a decrease in defect density is possible when no action has been taken regarding the previous day’s reported defects – worth reflecting on.

A suggested approach would be:

Once again, consider a cumulative defect count if this calculation is done on a daily basis.

Variances

As per your team’s need for more refined data, this defect metric can be modified.

• For a DD of High/Critical severity problems, consider the following formula:

Total count of High/Critical defects given per KLOC or modules

• This can also be utilized for recurring issues per module, in which case, only the persistent issues across builds/releases are counted.

Bug Density values leading to software refusal

Defect Density Industry Benchmark:

This differs across industries, applications, and teams. A definite threshold would be set in the manufacturing industry, but its application to the IT field would be significantly different.

DD alone indicates weak quality. However, the severity of individual defects influences the product’s suitability for use.

The presence of a high DD demands a detailed defect analysis and potential implications.

Ideally, who wouldn’t prefer a zero defect density? Therefore, despite the lack of a specific standard, the lower the value, the better off you are.

Closing Thoughts:

• It’s not a count for predictive analysis. The DD’s observed value cannot foretell the future product quality – it could be either high or low. Previous data will not pave the way for upcoming predictions.
• Amid crucial testing stages/cycles (like UAT), the DD is ascertained over time. For Example:: DD per first hour, DD per day, etc.
• Defect count through multiple releases/cycles can be assessed through per cycle or per release defect density.
• A simple depiction of tabular data might be as follows:

Wrapping Up

Defect Density is a fundamental measure of quality. Collecting and analyzing this defect metric proves to be a sound strategy. The added cherry on top? It’s one of the easiest calculations.

I hope this guide has given you enough knowledge to start exploiting Defect Density for better insight.

Writer: Swati, an STH team member, has penned this all-inclusive guide.

Is your team already calculating defect density? If yes, do you evaluate it per cycle, per module, or per KLOC? If not, are there resources that help you grasp quality? We welcome your comments, queries, and suggestions.