How to measure software quality

In Agile development, companies deploy code fast and often. It gives a company more profits and provides advantages. However, this approach may affect the quality of a product as sometimes companies sacrifice quality due to lack of time. 

 For many years companies measure software quality. Their goal is to estimate the product for compliance with quality requirements.

It helps release the product at a high-level quality, stand out among competitors and increase company revenue.

However, many companies failed to achieve software testing quality metrics. It happens due to poorly developed metrics that can't prevent risks.

We will show you how to organize and measure the effectiveness of software testing activities and software testing types.  

Software quality focuses and provides meeting quality standards and requirements. Software quality metrics is a reliable tool to measure how close to established requirements you are or prove a theory. Every project requires metrics that measure a level of quality. But the problem is that company can't implement all metrics in a project. Instead, they should develop their metrics depend on the project's goals. 

Companies that created products according to high-quality standards are more successful than competitors. Implementing and following software quality metrics helps to speed up the development process. It gives insights into how to improve performance and evaluate the following progress. 

To create metrics for the project, you should develop quality factors for them. Each metric is associated with quality factors that represent how quantitative it is. 

So, companies should create metrics for every quality factor to represent how quantitative it is.

According to Cem Kaner and Walter P. Bond, these metrics must meet validation criteria: 

• Correlation between metric and quality requirement
• Consistency between quality requirement and metric. If quality changes, metric changes too.
• If the quality factor changes in real-time, the metric changes equally too.  
• If we know the value of metrics at the moment, we can predict how to change the quality factor. 

To measure software quality, we should compare quantitatively between quality factor and metric.

At this point, a problem emerges: how do we quantify quality factor to compare with its metric. 

In software engineering, experts use two types of software quality metrics to solve the problem:

1. Direct metrics is "a metric that does not depend upon a measure of any other attribute." 
2. Indirect metrics or derived.

The difference between metrics is that direct metrics depend on one variable. Indirect metrics depend on various variables.

Examples of indirect metrics:

• Programmer performance;
• A number of bugs are identified during a specific period in one module (defect density). Many companies use defect density as software quality metrics. However, there is one problem associated with it. All failures and bugs aren't equal and cause by different conditions.
• Requirements stability 
• Total efforts spent on the project, fixing issues, etc. 

Another problem consists that some experts name one metric direct when they not. For example, IEEE Standard names Mean time to failure (MTTF) as one of the direct metrics. However, MTTF depends on the various variable as particular time interval, type, and the number of failures.

To develop valuables direct metrics, they should provide:

• definite goal (evaluating project status, estimation of reliability of the product)
• the particular scope of work (one project, single task, year of the team's work)
• a determined attribute that measured
• natural scale for this metric

We can highlight five quality software testing metrics.

1. Correlation between committed user stories and results that meet quality goals. 
2. Number of failures during STLC. An increasing number of failures during deploys can signal problems in the DevOps process. The metric should reduce with growing team skills and increasing experience. 
3. Test coverage. This metric shows how much of the code is covered by testing. Many experts argue about the efficiency of this metric. However,Google experts insist that the metric can be valuable information for evaluating risks and bottlenecks in a testing activity.
4. Defect Removal Efficiency (DRE). This metric evaluates the number of bugs after realizing the product and the number of bugs before release. It helps to track the increasing or reducing number of bugs.
5. Defect retest index. This metric shows how many new bugs are found after fixing bugs.