The software development space is extremely volatile and constantly changing. In software testing, what works for an organization now may not be as effective a few months later. As workloads become more distributed and decentralized, it is more difficult to test them and ensure quality. Today, organizations demand quality quickly. The time it takes for products to hit the market is getting shorter and shorter, and testing can sometimes seem more of a hindrance than a necessity. Due to this increased frequency of releases and all the different components and dependencies to be tested, QA teams are usually under great pressure. This can lead to inefficient testing, creating bugs that can go unnoticed for a long time. These bugs, if detected by users, can seriously damage an organization’s reputation. Some organizations still focus on manual testing, which can be time consuming, exhausting, and inefficient. So what are the new trends shaping modern testing? And do these trends mean for the future of software development?
1. AI and ML for testing
Manual testing is inefficient and shouldn’t even be considered when dealing with substantial microservice-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 impossible. Testers cannot offer all possible tests. As projects grow and more features are added to an existing workload, the area to be covered during testing also increases significantly, which helps in testing debt. When organizations fail to properly integrate testing into the development process, software ends up with bugs that go unchecked. As these new versions are released, QA teams have to deal with this huge backlog as well as testing new versions.
This is where AI can help you create tests to suit your use cases. Machine learning can be used to understand your code and to provide effective test cases and expected results. Machine learning can be trained to understand user priorities and run tests based on automatically generated test results. This can help provide maximum code coverage, allowing organizations to release their code without worrying about serious bugs making their way to production.
Another important thing that AI and ML can help is to eliminate the lag caused by regression testing. Regression testing is extensive testing of new code against old code to ensure that all features work as they are supposed to. However, regression testing for each release can take a long time, resulting in delayed releases. AI can help you by running automated tests based on ML-based priority. By focusing on more past testing, AI-powered 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 builds.
2. Tests without script
Testing budgets tend to be quite high in any project, and inefficient testing can result in costs well beyond your project’s budget. As stated earlier, testing can take a long time. But that’s a whole different ball game when it comes to writing test scripts for complicated, platform-independent workloads. With microservices in the picture, testers are expected to write test cases for various services that can be written in different coding languages. Quite often, members of the QA team are not familiar with all of the different platforms and coding languages. So how do you ensure that tests are performed efficiently and in a fraction of the time required to write test scripts manually?
Scriptless testing helps alleviate the pressure that QA teams face and allows them to create tests without requiring in-depth knowledge of different programming languages ââand platforms. All you need to do is define the test steps, and the scriptless test tool of your choice can then create a script based on those steps. The underlying test scripts produced by these tools are clean and completely abstract from the testers. These scripts are also reusable and therefore save time, effort and costs.
DevOps has helped organizations integrate and deploy their continuous workloads. While DevOps has significantly reduced time to market by bringing development and operations teams together, release quality becomes difficult to ensure. Rapid, continuous deployment is the name of the game, and testing can sometimes be put on the back burner. DevOps teams rush to develop new versions and leave testing to testers who are separate from DevOps teams. This can put a strain on QA teams as they have to test new versions at the very end of SDLC and still have to ensure good quality. Lack of visibility into the development process can become a challenge for testers when they have to navigate the entire code base and perform tests. With DevTestOps, QA teams are part of the SDLC from the start. The three teams work in tandem to achieve continuous integration, continuous testing and continuous deployment.
When testing is done early and often, bugs and flaws are identified more easily and can be immediately corrected by development teams before deployment. In DevSecOps, testers are not solely responsible for testing. Developers and operations teams also need to constantly test what they create. This way, bugs and flaws can be detected before you can proceed to the next SDLC step. Because testing is automated, testers can cover a much wider scope and can perform API testing, unit and integration testing, as well as regression testing.
However, this approach is still new and organizations should understand that implementing DevTestOps can be tricky. You can’t just integrate automated test tools into your existing CI / CD workloads. DevTestOps requires a significant culture change. Your DevOps teams need to be trained not only to deploy faster, but also to deploy quality software faster. Testing should be built into your CI / CD pipeline instead of adding it as a simple step at the very end.
Software testing and the need for speed and quality
As workloads become more complex, software testing can no longer be an afterthought. Gone are the days when you could ask testers to tinker with your code right before shipping it out. Today’s organizations need quality fast. Organizations spend a large chunk of their project budget on testing, and for good reason. However, as we venture further into the world of IC / CD, traditional testing techniques prove to be more expensive and less effective. Organizations need to see for themselves what works best for them. This can only be possible with a lot of research and PoC. However, these trends are some of the most promising that not only address the challenges faced by QA teams, but also help improve release quality much faster. When it comes to testing, there is no one size fits all. The right solution for your organization might even be a merger of various tools and approaches that target specific software testing challenges.
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