The software development space is extremely volatile and is constantly evolving. In software testing, what works for an organization in the present may not be as effective a few months down the line. As the workloads become more distributed and decentralized, it is harder to test them and ensure quality. Today, organizations require quality at speed. The time it takes for products to reach the market is getting shorter, and testing can sometimes seem more like a hindrance than a necessity. Due to this increased frequency of releases and all the different components and dependencies to be tested, there is usually a lot of pressure on quality assurance teams. This can lead to inefficient testing, creating bugs that can go unnoticed for a long time. These bugs, if found by users, can cause some serious damage to an organization’s reputation. Some organizations still focus on manual testing, which can be long, grueling, and ineffective. So, what are the new trends that are shaping modern testing? And do these trends mean for the future of software development?
1. AI and ML for testing
Manual testing is ineffective and shouldn’t even be considered when dealing with substantial microservices-based workloads. Organizations always aim for 100% code coverage when testing their workloads. However, in most cases, achieving 100% code coverage can be difficult, if not straight-up impossible. Testers cannot possibly come up with all the possible tests. As the projects grow and more features are added to an existing workload, the area to cover while testing also grows significantly, and this contributes to testing debt. When organizations fail to properly imbue testing in the development process, the software is left with bugs that are left unchecked. As these new releases are published, QA teams have to take care of this huge backlog along with the testing of new releases.
This is where AI can help with creating tests that work for your use cases. Machine learning can be used to understand your code and to provide efficient test cases and expected results. Machine learning can be trained to understand users’ priorities and run tests based on automatically generated test results. This can help provide maximum code coverage, thereby allowing organizations to publish their code without fear of serious bugs making their way to production.
Another important thing AI and ML can help with is by eliminating the delay caused by regression testing. Regression testing is the thorough testing of new code against old code to ensure all the functionalities are working the way they’re supposed to. However, regression testing for each release can be really time-consuming, leading to delayed releases. AI can help with this by running automated tests based on ML-based priority. By focusing on more prior tests, AI-based test suites can help save time. And if you add parallel testing to the mix, you can get more done in less time and gain more confidence in your releases.
2. Scriptless testing
Testing budgets tend to be quite high in any project, and inefficient testing can incur costs way beyond your project’s budget. As discussed earlier, testing can be time-consuming. But it’s a whole other ballgame when it comes to writing test scripts for complicated, platform-agnostic workloads. With microservices in the picture, testers are supposed to write test cases for various services that might be written in different coding languages. Quite often, QA team members don’t know all the different platforms and coding languages. So, how do you ensure that testing is done efficiently and in a fraction of the time it takes to write testing scripts manually?
Scriptless testing helps alleviate the pressure QA teams face and allows them to create tests without requiring in-depth knowledge of different programming languages and platforms. All you have to do is define the test steps, and the scriptless testing tool of your choice can then create a script based on those steps. The underlying testing scripts produced by these tools are clean and completely abstracted from the testers. These scripts are also reusable and hence help save time, effort, and costs.
DevOps has helped organizations to integrate and deploy their workloads continuously. While DevOps has helped considerably reduce time to market by bringing development and operation teams together, the quality of releases is getting hard to guarantee. Quick and continuous deployment is the name of the game, and testing can sometimes be put on the back burner. DevOps teams rush to develop newer releases and leave the testing to the testers who are siloed from the DevOps teams. This can cause a huge strain on the QA teams as they have to test new releases at the very end of the SDLC and still have to ensure good quality. The lack of visibility into the development process can become a challenge for testers when they have to comb through the entire codebase and perform tests. With DevTestOps, QA teams are a part of the SDLC from the very beginning. All three teams work in tandem to achieve continuous integration, continuous testing, and continuous deployment.
When testing is done early and often, bugs and defects are identified more easily and can be immediately fixed by the development teams before the deployment. In DevSecOps, testers aren’t solely responsible for testing. Developers and operations teams are also expected to test what they create constantly. This way bugs and defects can be found before they can move to the next SDLC step. As tests are automated, testers can cover a much bigger scope and can perform API testing, unit, and integration testing, along with regression testing.
However, this approach is still new, and organizations need to understand that implementing DevTestOps can be tricky. You can’t just integrate automated testing tools into your existing CI/CD workloads. DevTestOps requires a significant shift in the culture. Your DevOps teams have to be trained to not only deploy faster but to deploy quality software faster. Testing has to be woven into your CI/CD pipeline instead of adding it as a mere step at the very end.
Software testing and the need for speed — and quality
As the workloads become more complex, software testing can no longer be an afterthought. Gone are the days when you could have testers tinker with your code right before you shipped it off. Today, organizations need quality at speed. Organizations spend a huge chunk of their project budget on testing and for the right reason. However, as we venture further into the world of CI/CD, traditional testing techniques are proving to be more expensive and less effective. Organizations have to see for themselves what works best for them. This can only be possible through a lot of research and PoCs. However, these trends are some of the most promising ones out there that not only address the challenges faced by QA teams but also help improve the quality of releases much quicker. When it comes to testing, there is no one-size-fits-all. The correct solution for your organization could even be an amalgamation of various tools and approaches that target specific software testing challenges.
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1 thought on “Software testing trends: From AI to DevTestOps, what’s hot and why”
Nice writeup showcasing the software testing trends from AI to DevTestOps, Twain! By the end of 2024, 75% of organizations will have shifted from piloting to operationalizing AI, predicts Gartner. Rita Sallam, distinguished research vice president at Gartner, says, “To innovate their way beyond a post-COVID-19 world, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to succeed in the face of unprecedented market shifts”. Next-gen technologies are proving monumental in accelerating enterprise digital transformation and enabling organizations to facilitate process excellence. At the same time, software testing and QA also play a pivotal role in ensuring the tri-factor benefit of speed, value, and quality. Here is an insightful article that explains software testing in the world of Big Data, AI, Smart Machines, IoT, 5G, and Robotics: