The future of testing is bright

The initiative in giving the needed importance to testing has begun to receive the right attention. Due to the rapid developments in the digitalized industries, the need for proper software testing has also increased.

When we mention genuine testing, we mean that until recent years many software development companies were trying to reach the final product with old testing methods.

Often the role of the tester has fallen into the shadow of the developer, and this is due to the fact that many companies have not give priority to testing. Testing the software by the developers themselves and in the final stage, even if we use advanced techniques, the chances are high that dozens of bugs are hidden in the software.

Remember, by trying to make smaller expenditures on human resources, you run the risk of doubling the cost near to the final.

Software testing goes beyond the developer's imagination; this makes the collaboration methodology between the two parties even more challenging.

The last decade has seen a relentless push to deliver software faster. Automated testing has emerged as one of the most important technologies for DevOps.

Quality and access control are preventive controls, while others are reactive. In the future there will be an increasing concentration of quality because this prevents customers from having a bad experience. Thus, fast delivery of the required quality - or better yet, giving the right value at the right level of quality - is the main trend we will see this year and beyond.

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Let’s rank some trends which are furiously leading the field of testing:

Automation testing

Attempts at automated testing will take a more rapid turn. As mentioned above, many companies still operate with old testing techniques, including manual testing.

Human resources engaged to do manual testing at a critical stage of the project; this will likely slow down the job submission process. Automating manual tests is a long process that requires dedicated engineering time. In addition to exploratory testing, which must be done by individuals, automation testing remains one of the most promising trends for the future.

Data-driven testing

Immediately after automation, we continue to the data-driving testing for the same purpose; faster, more efficient results and this time based on data.

Like a proper scientific experiment, what makes data-based testing valuable is that anything that acts as a "variable" that comes with the ability to change. It is separated from the test and treated as an external asset. This means that the script is only focusing on application behavior when working with certain data values.

In the future, expect to see an increased emphasis by practitioners on data-driven decision making.

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When AI gains momentum

We usually encounter two problems; either we do not have enough tests or we have too many of them.

To create a UI test today, you either have to type a lot of code or a tester has to click through the UI manually, which is an incredibly tedious and slow process.

Test generation tools make the first hit on this issue. An example of artificial intelligence is the creation of Chat bots, where the Chabot plays the role of human.

They press buttons, browse pages, change photos, and so on. When they find a defect, they write a ticket in Jira as documentation for developers who need to take action to correct the defect.

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Machine learning and selection of predictive tests

While machine learning is often used synonymously with AI, they are not strictly the same. Machine learning uses algorithms to make decisions and uses feedback from people's data to update those algorithms.

Machine learning was created to make better decisions over time based on continuous feedback from testers and users.

Machine learning has tried to reach the world of E2E testing due to lack of data and feedback. E2E testing is typically built through human intuition for what is important to test, features that seem important or dangerous.

The new applications are using product analysis data to inform and improve test automation, opening the door to machine learning cycles to greatly speed up test maintenance and construction.

13/08/2021

In the next article we will read more about special emphasis automation and control through machine learning.