Artificial intelligence and machine learning are not the only changes that we should expect in software quality assurance, but they are a big part. One very happy trend for software quality assurance professionals is that software quality processes are gaining more respect. Testing is becoming more and more a first-class citizen, as said by Diego lo Giudice, vice president and principal analyst for Forrester software development and delivery area that covers DevOps and agile.
As enterprises are trying to deliver software faster by testing and deploying it more quickly, software testing has started being recognized as a critical part of the development process. It means that organizations are trying to learn that testing does not slow the delivery of software.
The tools are getting better, and so are the processes.
The majority of teams are still working as they did in the ’90s, but now it has started to change. DevOps started to be used in continuous integration, but progress in testing won’t come without a cost. We must put real effort into the design, and we have to have more robust testing tools. Our systems are so complicated that we need those workflow analyzers to work with continuous development.
One change in testing could be the adoption of tool-assisted testing and production. As it is very new, it will be more common to test live production code not to affect the customers. A team-based environment system is also required where testers will be an integral part of the development team. But perhaps the most meaningful change in software testing in both the short and long term is the use of AI and ML.
Artificial intelligence is changing what’s automated
AI and ML are a very important part of software quality assurance jobs, but where will it be by the end of 2020. According to the co-author of agile testing contents and various other books on software testing, a very strong move for more AI and ml and away from manual methods.
Teams are required to increase their use of automation, and to do that, they should increase the use of artificial intelligence and machine learning. Testers are not only using AI for increasing the level of automation. Smart teams noted, not everything that involves testing needs is required to be automated. The whole development team should be applied, and teams are required to know when it makes more sense to perform testing using manual processes versus when automated testing makes the most sense.
The testers are made smarter not by replacing them with automated testing but by utilizing automation to help them do a better job. This should include putting testing into the developer’s hand in a test-driven environment using tools as cucumber and Gherkin. Cucumber ads in automated testing and anchorages collaboration in a team environment where Gherkin is in its editor to build natural language instructions.
We should always keep looking for software quality assurance tools to take advantage of AI for streamlining the testing and helping testers keep up with developers. The rate with the software development proceeds indicates that people can’t keep up with testing. We should leverage machine learning to close that gap.
Automatic intelligence ads and continuous testing allows companies to push their products and apply them while still testing. For example, if a team uses AI in user interface testing for making sure advertising is in place, trying localization, and performing e-commerce testing. We already have manual testers but moving on to automation adds more intelligence.
Case in point automatic robotic process
Another trend to look into is robotic process automation, which requires software robots for developing code for business processes. RPS stands for a type of business process automation that seizes data and triggers automatic responses. Software robots could be instructed to perform tasks that humans currently perform but which humans find mind numbering. Automation allows human teams to do more challenging work.
The majority of clients used quality assurance software testing automation capabilities for their tools for doing robotics automation. There is a growing sensibility for starting to think about building robotic process automation without testing. But it that’s not a good idea as allowing RPA to move forward without testing produces various kinds of risk, including notable misuse of RPA, that could result in implementation failures and could also lead to security exposures.
RPA models existing tasks are considered good, but if the task is being done wrong, it models at asking what’s being done wrong. As those failures could be costly or worse, Quality assurance of software robot processes is as just important as any other type of software quality assurance jobs. Acknowledging that RPA is is contemplated by some to be the quickest growing type of automation expected in 2020, the need for quality assurance software testing converts to be even more important.
But RPA could also serve as a useful test tool as we can use RPA or functional testing and then automate other software quality assurance tools in the distribution chain that could easily be integrated.
Expect more testing in the cloud.
The legitimate place for this kind of testing is in the cloud, where robots are readily available, and an increasing amount of development effort occurs. As cloud adoption is growing very fast, testing using the cloud environment is a very important trend. There is too much time spinning up the test environment, and now it self service and cloud-enabled.
The selection of cloud-based quality assurance software testing is growing interesting questions about data security. We are currently working on new hybrid deployment choices that could address some of those challenges while still providing the same advantages that come with cloud-based scale.
So, which of these components suggest that quality assurance software testing is a part of the future. To a large extent, as artificial intelligence and machine learning is important to test, the complex process is on the horizon, and a very great time testing tools will be cloud-based and will use RPA.
But not everything will work that way as not all systems are appropriate for all robotics. Not all testing makes sense for automation, and AI depends heavily on extremely accurate data where humans could perform tasks in left exacting situations. Software testers will still be around, and they will have some powerful tools to work with.
As we can see there are a lot of things going out in the world of software quality assurance. So introducing important changes and new software quality assurance techniques for our Quality assurance processes is fundamentally a matter of Survival if we want to keep up with our competitors and convince a potential client to choose us over them.