When talking about AI, one of the biggest concerns is the impact it could have on jobs, both now and in the future. There is a very real fear, that with machines proving to be much more efficient at many tasks, the role of humans will become obsolete. However, it is not all doom and gloom. Specifically in the field of software testing there may still be several important roles for humans to fill that can’t be matched by machines.
It requires a different approach to working synergistically with AI, using it to leverage better use of human resources and expertise. In other words use bots to do the grunge work, taking advantage of their ability to operate 24/7, process vast data sets and different parameters. Let’s consider three examples of what this would look like in terms of a practical application for software testing.
1 – Testing coverage & workloads
A common issue in software testing is that as a project develops the parameters for testing often increase, creating additional workloads for the testing team who are limited in their capacity and the number of hours they can effectively operate for.
However, having AI testing bots at their disposal, testers can reprogram the tests to include new parameters and the scope of the testing can increase without adding significantly to the workload of the testing team. AI testing bots can also be programmed to run parallel tests and auto-tune these as the project evolves.
Effectively software testers can have a full team of bots running a broad range of tests while their task is primarily to manage, analyse and program the testing process.
2 – De-bugging effectiveness
Considering that AI bots can work effortlessly 24/7, they can be put to very effective use de-bugging programmes overnight or over weekends, thereby increasing the scope and time that the tests run without requiring human input. In the morning the testing team can then analyse and triage the test results and set about resolving the issues.
Even more advanced integration can see bots automatically rewriting the code to fix the bugs or predicting potential weak spots based on historic testing results.
3 – Advanced real-time testing
Using AI bots in real-time testing can increase the scope of real-time testing ability. One example is using bots to report deviations or identify and clean up polluted data. Once again using bots to do the grunge work can improve the quality of the testing and help the testing team to operate more effectively on projects.
What does the future look like?
The integration of AI bots into testing won’t happen overnight, it will require testers to be retrained to use bots effectively and adapt traditional testing processes to manage time and resources more effectively.
In the process there is an opportunity for testers to develop and improve their expertise, learning how to integrate their knowledge with new technology to create more effective and efficient testing parameters.
Despite the benefits that bots offer software testing, at this stage humans still have a vital role to play in terms of programming tests, analysing results, managing the process and attaching meaning to it. The role of software testers is far from becoming obsolete, at least in the foreseeable future, as long as testers can learn to adapt and take advantage of AI technology that becomes available.
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