Over the last decade, there has been a constant effort to deploy software more quickly. Companies are spending a lot of time and effort on end-to-end software delivery pipelines, and containers and their ecosystem are delivering on their early promises.

High performers have been able to provide software faster than ever before thanks to the combination of delivery pipelines and containers. Nonetheless, many businesses are still attempting to strike a balance between speed and quality. Many companies are stymied by antiquated software, extensive test suites, and shaky pipelines. So, how do you proceed from here?

End consumers have become software testers as a result of the pressure to deploy promptly. They don’t want to be your tests any longer, and firms are taking notice. Companies now want to make sure that speed does not come at the expense of quality.

Testing is one of the most important DevOps controls that businesses can use to ensure that their customers have a positive brand experience. Access control, activity recording, traceability, and disaster recovery are some of the others. According to studies conducted by our organization over the past year, long feedback cycles, slow development loops, and developer productivity will continue to be key objectives in the next years.

Some controls, such as quality and access control, are proactive, while others are reactive. In the future, there will be a greater emphasis on quality because it prevents customers from having a negative experience. As a result, the main trend we will see this year and beyond is producing value quickly—or, better yet, offering the correct value at the right quality level quickly.

Here are five crucial trends to keep an eye on.

1. Automating tests

Efforts to automate testing will continue to grow. Manual tests are still used in a surprising number of firms’ delivery pipelines, but you can’t deliver fast if humans are involved in the critical path of the value chain, slowing things down. (The only exception is exploratory testing, which requires the use of humans.)

Manual test automation is a time-consuming process that necessitates dedicated engineering time. While many organizations have some test automation in place, there is still work to be done. As a result, automated testing will continue to be a popular trend in the future.

2. Establishing a continuous quality culture

Quality must become part of the DevOps mindset as teams automate tests and adopt DevOps. As a result, everyone in the organization will share responsibility for quality.

Teams will need to be more deliberate about where tests are placed. Should they move tests to the left in order to catch problems sooner, or should they add more quality controls to the right? Chaos engineering and canary deployments are becoming increasingly important on the “shift-right” side of the house.

Large test suites are tough to move to the left because you don’t want to generate long delays while executing tests earlier in your process. Many firms mark some tests from a big suite for pre-merge execution, but the problem is that these tests may or may not be relevant to a particular changeset. Predictive test selection (see trend #5) is a persuasive option for executing only the tests that are relevant.

3. Data-driven DevOps

The industry has spent the last six to eight years focusing on linking multiple technologies by creating powerful delivery pipelines. Each of these technologies generates a large amount of data, yet it is only used infrequently, if at all. In the growth of tools in delivery pipelines, we’ve progressed from “craft” or “artisanal” solutions to “at-scale” solutions.

The next step is to add intelligence to the tooling. Expect practitioners to place a greater emphasis on making data-driven judgments.

4. The rise of AI: Test-generation tools

There are two major issues with testing: there aren’t enough tests and there are too many. The first challenge is tackled by test-generating tools.

To develop a UI test today, you must either write a lot of code or have a tester manually click through the UI, which is a very uncomfortable and time-consuming procedure. Test-generation tools employ AI to design and perform UI tests on a variety of platforms to alleviate this agony.

One technology my team looked into, for example, employs a “trainer” that allows you to record activities on a web app in order to construct scriptless tests. While scriptless testing isn’t a new concept, what makes this tool unique is that it “auto-heals” tests in response to changes in your user interface.

Another technique we looked into was AI bots that mimicked human behavior. They notice problems by tapping buttons, swiping images, typing text, and navigating screens. When they discover a problem, they file a Jira ticket for the developers to address.

5. Machine learning and predictive test selection

Apart from test generation, AI has various applications in testing. An innovative method called predictive test selection is gaining interest for enterprises battling with high test suite runtimes.

Thousands of tests run continuously in several firms. It could take hours or even days to gain feedback on a tiny adjustment. While having more tests is generally beneficial to quality, it also implies that feedback is delayed.

Google and Facebook, for example, have built machine-learning algorithms that analyze incoming changes and only execute the tests that are most likely to fail. Predictive test selection is what it’s all about.

What’s particularly impressive about this technology is that you can run between 10% and 20% of your tests to achieve 90% confidence that a full run will not fail. This allows you to condense a five-hour post-merge test suite into 30 minutes on pre-merge by just performing the tests that are most relevant to the source changes. Another possibility is to cut a one-hour run down to six minutes.

The real problem: Test execution time

Automated testing is sweeping the globe. Despite this, many teams are having difficulty making the move. The DevOps philosophy would include a continuous quality culture. Tools will continue to improve in intelligence. Test-generation tools will aid in the transition from manual to automated testing.

For more info: https://mammoth-ai.com/testing-services/

Also Read: https://www.guru99.com/software-testing.html