What is the Future of Software Testing in the Era of AI and ML?

We are using AI and ML-based applications in our everyday life, consciously or unconsciously. Smartphones, smart cars, drones, social media feeds are some of the examples of AI in use in our everyday life. Smartphones have become an integral of our life. Siri and Alexa have become part of our family.

Hope everybody is familiar with Siri, Apple's sweet and smart personal assistant. Siri is a perfect an example of Artificial Intelligence and Machine Learning put together. Yes, the AI personal assistant uses machine-learning technology to get better and smarter every day.

Now that we are using AI in our everyday life, what will be the role of AI and ML in software testing, a field that has been here for decades now?

We have seen the capabilities of AI in the past. There are plenty of case studies on AI. Here are a couple of examples. AI has defeated top poker players; even Elon Musk's AI bot trashed the best gamers on their game.

The Software Development Lifecycle is getting complicated every day. The delivery times are getting shorter and software testers need to give feedback to the developers instantly. Releases that happens once a month previously is now happening weekly.

Given the pace at which the new software launches, you are left with no choice, other than to adapt to the changes and become smarter.

The new age testing is here!

With the introduction of AI and ML, software testing will become even better. AI with the help of ML can write its own line of code by stealing from existing software. It can also be used for test execution and maintenance.

Test automation and regression testing will only be smarter, faster and better with the introduction of AI. The testers can stop worrying about the usual boring tasks and start concentrating on better strategies.

Will AI & ML kill software testing?

No, AI & ML will not kill software testing, it will only get better with the help of AI & ML. Software testers should not fear AI, instead, they should be thinking of a practical way to incorporate both AI & ML in their work, this will help them achieve better results. AI will help you identify bugs quicker and faster.

Let's say organizations are preferring AI testing apps over humans to run test cases. Though these AI apps will deliver precise results, they lack some of the key aspects such as, scalability, performance, management, documentation and security, which can be provided by humans alone.

The impact of AI & ML in software testing

We have seen the capabilities of AI and ML in the past and what it can do in the future. The software industry, in particular, will see a lot of changes.

The software testers are not here to compete with AI & ML. Rather they are going to help in enhancing AI and ML-based tools.

In my opinion, Artificial Intelligence and Machine Learning will broaden our horizon and opportunities.

The speed at which the organizations demand their software to be launched is outrageous. Not to blame the organizations, the competition makes them take such decision. Thus, the necessity for software to be developed and tested quickly. Here are some of the changes you can expect to see in software testing with the introduction of AI

•  AI will boost accuracy. Just like test automation, but even better.

•  AI will expand the overall length and scope of testing.

•  AI will enhance the quality of the software.

•  AI will ensure a faster turnaround.

Is this the end for Manual testers?

AI is a milestone in the software industry. However, it is creating uncertainty amongst the manual testers with respect to their job. Manual testing is one of the oldest and traditional methods of testing. However, will software testing be the same way as today is the biggest question? The answer is, it will not be the same, but then you need manual intervention to design testing strategies. In the future, both manual testing and AI will coexist. However, the software testers will need a different set of skills to survive. They should build their data science skills and should be able to understand how Machine Learning works.

Conclusion

AI and ML have shown the world its capabilities in the past and the future looks exciting. However, we should be cautious while developing AI for advanced stages. Keeping the software testers in the centre and building AI assistance in the outside is the way forward in the present era. By following this approach, both humans and AI can definitely coexist. Hence, software testing will get better in the future with the assistance of AI.