A software test metric is a yardstick for tracking the effectiveness of quality assurance efforts. First, you establish metrics for success throughout the planning phase. Then compare them with the metric received after the process is complete.

However, many QA and software testing experts tend to focus on how the tests will be performed rather than the actual information produced by the tests. By this I mean that testers often focus on the simple satisfaction of completing all the tests. But is it still a good thing? You can have a 100% pass rate with all the green indicators on your dashboard and there is always a possibility that your tests are not strong enough.

This article will discuss five software testing metrics that could help QA professionals assess their success.

Characteristics of a “good” test measure

Let’s talk about the features that a metric should ideally have.

Related to business objectives

Critical KPIs should reflect the primary mission and purpose of a business; for example, monthly revenue growth or the number of new users. Each company chooses its metrics based on what it intends to achieve with its product. While it may seem appealing to pass all the tests, focusing on the wrong goals can be misleading. This can impact the work of the application and the entire complex system, such as the headless commerce architecture.

Allows growth

Each metric must allow for improvement. What if you achieved a 100% success rate? The goal may be to keep the metric at this level or to improve it further.

Encourages the development of a strategy

When a metric gives a team a goal, it also motivates them to ask questions to develop a plan. Suppose you need to increase your income. Determine if the product requires new features to encourage more purchases. Is it necessary to create a new acquisition channel? Has the competitor launched any new products or features that attract new buyers?

Traceable and understandable

Good indicators are easy to understand and follow. If not, how will the people who bring them together make informed decisions? Employees need to understand what they can do to improve results.

Three tips for choosing and measuring software test metrics

1. Start by asking questions

Your questions should relate to three topics:
1. What you measure
2. Strategies and tools to measure it
3. Reasons to follow it

To avoid analyzing unnecessary metrics, pay attention to the process of defining metrics. Sometimes a small number of backlog bugs mean your QA team is doing their job. However, when you break down these bugs into high / medium / low priority issues, you will be able to better see the overall quality of the program and make any necessary adjustments.

2. Don’t overlook automation when calculating quality assurance metrics

Automation saves you time on manual data collection and helps ensure that your metrics are always relevant. Suppose you are using Jira. Set up a Jira Query Language (JQL) query on your Confluence page if you need critical bug data every sprint. It will be updated frequently. Or you can use other tools depending on your preferred test management / task tracking system.

3. Collect feedback and gradually improve metrics

Once you have configured and gathered all of the metrics, the feedback and improvement processes begin. Pay attention to comments to improve the efficiency and clarity of your metrics and reports.

Five software test metrics to track

Now let’s look at some specific examples. Note that different aspects of quality matter to varying degrees depending on the circumstances.

1. User satisfaction

Here you will want to see the customer’s reaction to the product. You use user satisfaction surveys and support tickets that reveal bugs. If you follow these quality metrics and work to improve them, the business will grow as you see more satisfied and loyal customers. If something is wrong, you will need to do a causal analysis of the problem and remove the obstacles.

2. Process metrics

These are internal measures that have a significant impact on the quality of your product. For example, you can track the lead time and the time it takes from defining the task to deploying and producing code.

Another metric you can use is cycle time. This means that it’s time to create a feature after receiving approval to start working on it. Finally, you can track the time it takes to resolve the difficulties. This could refer to how quickly tickets or bugs are resolved once they have been reported.

Because these metrics can be difficult to measure, another way to improve process efficiency is to detect where unfinished work is starting to pile up in the queue. This can highlight a bottleneck that, if removed, could help your teams become more productive.

3. Coverage indicators

Another indicator of test quality is test coverage. It informs us of the amount of code tested. This is a method of making sure that your tests check the code and how well they work. In this case, it is better to use a top-down strategy. The first step is to analyze the coverage of the module. Then you consider the functionality and, finally, the data coverage in each functionality. That means how many different combinations of potential data entries you cover with tests.

This group includes measures such as:
● Percentage of requirements coverage
● Unit testing coverage
● Coverage of manual or exploratory tests
● Test cases by category of requirements
● User interface testing coverage
● Integration and coverage of API tests

4. Code quality indicators

Evaluating code quality means categorizing code value into two categories: good and bad. There is no single notion of quality because almost every developer defines for themselves what constitutes good code. How to assess the quality of the code? Tools like SonarQube allow you to reveal the amount of technical debt in a system. You will need to rank the issues and vulnerabilities, organize them by priority, and select what you are going to focus on.

5. Indicators of bugs or incidents

Every problem differs in severity, so don’t give all problems the same weight. Some issues are just suggestions for improvement. Determine which components of quality are more important than others to your business. That said, go beyond the number of flaws when analyzing the metrics you’ll be using.

What can you extract from incident reports? These results may include:

● Total number of bugs
● Open faults
● Closed faults
● The closing time for each incident report
● Changes since the last version

Software test metrics measurement rules

Evaluating metrics in software tests and estimating their success can be frustrating and vague. Here are some tips and suggestions you can use:

1. Correlate your metrics with project, process, and product goals. Keep in mind that a single indicator is not enough for a complete view of the quality of your software.
2. Track progress (or regression) over time. Streamline the data collection process with automation, store the data in a collaborative resource such as a Wiki / Confluence, and regularly review the results.
3. Report statistics to the client and the team to show your progress. Reports should be easy to understand, so make them useful and user-friendly.
4. Check if the metrics are valid. Keeping track of irrelevant metrics and displaying inaccurate data is out of the question.

Measurement is an important activity in software testing, such as determining the number of successful tests versus the number of failures. All the information you get is passed on to the stakeholders. As a result, they can make informed decisions such as when to publish an app.

How can you monitor your testing activities? You need to determine the relevant software test metrics. Choosing the right test metrics can be difficult. Often, teams opt for metrics that are not in sync with the entire organization.

What can cause the lack of adequate benchmarks? Stakeholders fail to measure progress, identify opportunities for development, or control which testing tactics have the most positive impact. All things considered, QA teams should track individual progress, skill level, and success, as well as code quality, bugs, and coverage.


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