Let’s face it. The market for software products like desktop applications, games, and web services is very saturated at the moment. Unless your process can provide your consumers a good experience, it will not be able to garner many users or gain positive feedback at all.

Luckily, we can address many issues such as pesky bugs and other defects during software development through the testing phase.


Big companies like Google and Facebook use automated testing for quality assurance, while some companies have dedicated testing teams who can manually do the same. For smaller tech firms, the developer would either be the tester themselves, or they can outsource this process through services like

Metrics and its Importance

Software testing by itself is useless if you produce no information from it.

That is where the metrics for testing comes in. Testing metrics use numbers to score the quality and effectiveness of a testing process during development. And it is a quantitative method to extract information regarding the current state of the product.


Metrics are like a unit of measurement that is used to gauge the competency of a product during development.

It also allows you to track the progress of not only the process but also the developers who work in it. That reduces the possible error they may commit as the metrics allow for gauging their efficiency, which leads to a more nuanced control.

Metrics are not only a single indicator. There are tons of examples of them out there that you can implement, and you must focus on the right metric to gain more from this cycle of development.

Before we proceed to the types of testing metrics, let us talk about the base and computable metrics.

The Base Metrics and The Computable Metrics

Base Metrics

When you capture raw data and haven’t done anything to process them yet, we call those base metrics. Some call this value absolute metrics as well.

Examples of this are the total number of testing cases and the total number of testing cases executed.

Calculated or Computable Metrics

When raw data are processed and used to calculate other values, we call the results computed metrics.

Examples of this include the range number of test coverage and the estimated efficiency of the task.

Types of Metrics

We have different types of testing metrics out there, namely:

  • Process Metrics
  • Product Metrics
  • Project Metrics

Process Metrics

Data extracted from the process metrics are used in raising the efficiency of the cycle of SDLC ( Software Development Life Cycle). Here are some examples of process Metrics and their formulas:

Cost of Quality

It is the calculated extra value of the results of using quality resources as an investment for the entire process, and we express this value in financial terms.

Cost of quality = Cost of good quality + price of poor quality

We can also expand it to:

CoQ = (Prevention Cost + Appraisal Cost) + (internal Failure costs + external failure cost)

We can also use this formula:

CoQ = ((review+testing+vr+vt+QA+cm+measurent+training+rr+rt))/overall effort × 100


vr = verification testing

  • vr = verification review
  • vt = verification testing
  • va = quality assurance
  • cm = configuration management
  • rr = rework review
  • rt = rework testing

Cost of Poor Quality

This value pertains to the consequences of using poor quality initiatives on the process/products. This is the price of all errors accrued during the testing.

Cost of poor quality = (overall cost of reworking/ overall effort before and after rework) x 100.

Cumulative cost of reworking = cost of repairing + cost of revising + retest cost + patch-related costs

Defect Density

This value refers to the abundance of flaws found while developing the projects with concern to the extent of the size of the entire scheme.

The defect density of a project = cumulative number of flaws/ extent of the project measured through KLOC or FO

Review Efficiency

It is the efficiency of utilizing and detecting flaws found from the reviews during the verification phase.

Review efficiency = ((amount of flaws captured through review)/ cumulative volume of defects detected) x 100.

Testing Efficiency

It refers to the competency of the testing process measured through the ratio of defects found in testing against the errors found during user acceptance

Testing efficiency = 1 – ((flaws found during user acceptance)/ total number of testing defects) × 100.

You can also use:

TE = (overall defects before user acceptance/ overall flaws with user acceptance) × 100

The efficiency of Fixing Defects

Measures the performance of detecting errors before they can even reach the users.

Efficiency of Fixing Defects = (1 – (tallied value of flaws spotted by customers/ cumulative quantity of defects)) x 100.

Residual Defect Density

This pertains to the percentage of errors detected by the customers.

Residual defect density = ((gross amount of defects found by customers/ gross amount of defects including customer-found defects) x 100.

Product Metrics

This metric refers to the overall dimensions of the product.

Data taken from the product metrics are used to deal with and increase the quality of the product. This also involves handling bugs and errors before the end-users can spot them. Below are some examples of commonly used product metrics:

Code Length

We measure the size of a software product through the length of its code via line code measurements like KSLOC and FP. Generally, as this metric increases, so does its complexity and risk of being exposed to errors.

Client Retention

This value reflects the quality of your product by measuring how many clients are doing business with you repeatedly. By keeping this metric high, you will be able to cut additional costs resulting from initiatives to attract new users.

Churn Rate

This metric is the complete opposite of client retention. It refers to the rate of clients who suddenly stopped using your service due to updates and changes you applied to your product. Keeping track of this value can tell you what defects may be plaguing your products.

Rate of Feature Usage

This value derives the rate of usage of certain features available in your software. This can help you decide the elements that you have to retain to keep your users. You can also use this metric to gauge if a specific aspect of your software is popular during particular seasons.

Customer Lifetime Value

This is an essential tool to measure how profitable your product by forecasting how much fortune you can earn from your customers. Keeping this value high will ensure a stable and profitable venture. It will also help you know whether if it’s worth investing to earn customers via marketing campaigns.

Project Metrics

Data brought from it is used to assess the efficiency of the teams and tools involved in the software development project.

Calendar/Schedule Variance

This metric refers to the discrepancy between the planned date of project phase completion and the actual date of realization of that cycle.

SV = ((date of phase realization – intended date of completion) + start variance) / intended date of completion × 100

Effort Variance

This is a measure of the discrepancy between the intended amount of effort for the project and the actual amount of effort needed to be exerted for task completion.

EV = ( amount of effort needed to be exerted – the estimated amount of effort)/ estimated effort × 100

Project Productivity

This metric measures efficiency by correlating the total output achieved to a unit of input invested.

PP = Project Size/amount of efforts exerted for the project

Choosing the Right Metrics

As stated earlier, there are many examples of testing metrics available for usage out there. But, that does not mean that you can just use any metrics to measure the quality of your products. In choosing the right metric tool to use in testing, you need to consider the following:

  • What is your primary goal?
  • Which metrics can help realize improvements?
  • Which metric is both agile and optimum