Over the last decade, companies have seen in a hurry to deploy or deliver software more quickly. Today, Automation testing has become one of the most crucial technologies for scaling DevOps. Due to this, more and more time and effort are invested in the building of end-to-end software delivery pipelines, containers, and their ecosystems that also help fulfill the early delivery promise.
Moreover, the blend of containers and delivery pipelines allows high performers to deliver software before the expectations of their customers. Overall, if we look at the market, you may see many organizations are struggling to find the balance between speed and quality. However, some of them try to make headway with large test suites, legacy software, and brittle pipelines.
On the other hand, many software development companies don’t have much confidence, and they believe that they can’t perform testing well. According to their mindsets, the influence of quality defects is substantial.
Due to this, they invest most of their time in quality assurance, and even after making many efforts, they fail to achieve the desired outcomes. And, this thing has not happened due to a lack of talent or a company’s experience but occurs because their technology doesn’t support them well at the time of software testing.
That’s why at the time of releasing quickly, nowadays, end-users play the role of software testers. On the other side of the coin, one of the biggest aims of companies today is to assure that quality will never be compromised in the pursuit of speed.
But, what do you think is the software released successfully? Well, it is difficult to say until the software has been thoroughly and properly tested because testing sometimes requires a wide array of resources that further needs the time and budget to get the job done right. Similarly, in this case, it becomes necessary to fill the gap by leveraging Machine Learning because it allows redefining the future of software testing.
Furthermore, due to the evolving nature of Machine Learning, testing is one of the top DevOps controls that companies can utilize to ensure that their customers will get a delightful brand experience. Similarly, according to us, Quality and access control are preventive controls, whereas other factors are counted as reactive.
So, if you want to improve your product’s quality and protect your customers from bad experiences, you should try to deliver the right value faster. And, this is the new normal or the key trend that you can expect this year and beyond.
In this blog, we would like to demonstrate how ML will help enhance the future of software testing; or you can understand, we will describe some key trends in detail to help you bring a new transformation in the era of software testing.
Accelerating Automation Testing
In 2021, test automation efforts will continue to accelerate to deliver faster than expected. However, some companies are still implementing manual testing and using traditional methods in their delivery pipeline.
Yet, if you want to deliver fast, you should automate tests as much as possible. At some places, like exploratory tests, you will require humans to get the testing done. Otherwise, it would be best if you considered automation testing to save your time and efforts.
In some companies, many software testing experts have their own test automation tools, and some of them are using open-source and paid tools, but you can understand this trend will remain the same in the software testing domain. People will highly prefer automated testing to stay ahead of their competition and satisfy their high-demanding consumer needs in a shorter span of time.
To grab more information, give a quick read on how machine learning can be used in software testing.
Develop a Continuous Quality Culture
Since automation testing is the preference of almost all software testing companies, they are also adopting DevOps faster.
Therefore, attaining quality for their customers will become a shared responsibility of everyone in the organization. For this, one should know about shift-left testing to catch issues faster, and they should where tests are needed to be landed, which means one should maintain the right quality control in order to groom the scenario of software testing.
For the past few years, the major focus of industries has been using various tools for building robust delivery pipelines. Nonetheless, each of these tools produces a vast amount of data, yet that is being used rarely.
However, in order to get visibility and detailed metrics of each aspect of your digital business, such as applications, infrastructure, and customer experience, there is a need to move from “artisanal” or “craft” solutions to the “at-scale” stage in the advancement of tools in delivery pipelines. Also, you must right-size your tests with analytics to make data-driven decisions.
Autonomous End-to-End Tests
One of the core advantages of Machine Learning in E2E testing is to leverage high complex product analytics data to gauge and evaluate user needs. With ML-driven testing, it is feasible to monitor every single user interaction on a web application.
Also, you can understand common journeys that your customers walkthrough, and you can ensure that these use cases will work as supposed to. Furthermore, if the machine is testing a lot of applications, then it can surely gain a learning experience from all of those applications and may understand how the application help impact the user experience.
When it comes to using ML-driven testing, you can develop more meaningful and better tests than humans that consume so much time and make many mistakes while making changes in the application. Therefore, autonomous end-to-end testing has the potential to become one of the key trends to shape the future of Software Testing.
Also Check out our Ebook- Software Testing Future Roadmap
Looking for some expert assistance on Machine Learning based Testing Solutions? BugRaptors can be the helping hand you need to foster your Machine Learning Testing operations.