AI is a revolutionary technology, and in the right hands, it can do wonders for humans. But every technological innovation comes with two sides, and there is always a side that raises concerns
for people. AI is no different, and it also raises some serious concerns wherever it gets used. Many people think AI is here to replace, but that is not true. AI-powered software testing services work only because there are exceptional programmers and QA engineers who can build such AI models to reduce the time taken to complete any testing process. If you are new to the world of AI and its applications, you might be fascinated with all the work that can be done through AI, but before you use it for everything, you should also look at the other end and understand the ethical considerations of using this technology. In this article, we will look at what is AI-powered software testing, what are the benefits of using this, and what ethical considerations should be considered while working on AI-driven test automation.
What is AI-powered Software Testing?
AI-powered software testing is the process wherein you use testing tools with AI capabilities, or the entire process is done with the help of AI. AI can help testers to develop test cases and run
them without any human intervention. Moreover, the results can also be saved at a predefined location so that QA engineers can refer to them at later stages. AI-powered software testing is quite life-changing for many QA engineers, so let’s have a look at a few of the benefits of this change.
Benefits of AI-Powered Testing
Better Software Quality
With the help of AI in QA, you can improve the quality of your software by many folds. AI models can help you develop robust test cases and execute them. The models can find challenging
situations for the application and test them in a way that the application is tested from all angles. This reduces the chances of bugs going unnoticed and manual errors.
Test automation has always improved the performance of any testing process, and with AI included in this, it becomes even better. AI models decrease the dependency on human engineers to execute or design test cases for the application. Moreover, AI models can perform more tests parallelly than humans, and the overall time taken to complete the testing process is also reduced. This time reduction results in better performance for the entire team, and they can deliver working software faster.
Building AI models is a one-time activity that can result in significant cost reductions. When you do manual or automation software testing, you need to hire a team, and the team will take time
to test the applications. On the other hand, once an AI model is developed and deployed, you don’t need to have a huge team to run the testing process. Moreover, by adopting the process
of AI-powered software testing, companies can reduce their dependency on QA engineers and large teams. This will ultimately reduce costs for any team.
When QA engineers and Manual testers keep on testing the same thing repeatedly, it is possible that they miss out on some bugs. With AI-driven test automation, you never have to worry about accuracy. If you have developed an accurate model, the reports and results that it provides for any testing process will be way more accurate than QA teams.
Having known about the benefits of AI-powered software testing, you might be excited to use this technology for your business, but first, you should understand the ethical considerations for
this. So, let’s have a look at them in the next section.
Ethical Considerations in AI-Powered Software Testing
Impact on QA Engineers
Many people think that AI ML will replace humans entirely. This is not true; QA engineers will always be required, and AI models will never replace them completely. But many companies are
not ready to understand this. They are trying out different AI and ML-based tools, and with some signs of success, they think that AI will be the solution that will help them operate with lean
teams. By such thinking, organizations may start to reduce their QA teams, and this will directly impact QA engineers. AI-based testing tools can only work best if they are accompanied by
AI models can make decisions, but they aren’t aware of the real-world results of their decisions. Hence, there should be a mechanism to place accountability. This can be done by keeping
human engineers at the center of the testing process, and they should be able to verify everything that is done by the AI models. Moreover, humans should always be in a position to
control the testing process at any stage to ensure that nothing goes wrong during the testing of the application.
Bias is a common ethical concern with AI models. As the output of any AI model is based on the underlying data and its way of learning, it is important that AI models are not trained on biased
data. When an AI model is trained with biased data, it will always give out biased results, and in the case of testing, you don’t want to have biased results. If you have biased test results, there are chances that you may miss out on bugs, too.
AI-powered testing tools may not understand the difference between private data, and they can collect and process private or sensitive data without any hiccups. This can become a huge
privacy issue as you may end up processing people’s private data as a part of the testing process. Hence, it is essential to safeguard private data and try to keep AI models in check so
that no privacy lapses take place.
When you develop an AI application in-house, you may know the solution entirely, but that is not always the case. In AI-driven testing, you might use third-party tools, and they will have their
own algorithms, which might not be open-sourced. Transparency is a concern in AI apps, and you should always choose AI tools that are transparent so that you can look around and
understand the application or algorithm easily.
Coming to an end, while AI for QA will help a lot of testers, and every company should try this technology, the ethical considerations should not be overlooked. Just like every other technology, AI also has a dark side, and you should always be careful while building solutions.