Top 5 Ways that Artificial Intelligence Would Change Software Testing

The area of testing software has never been so broad. These days, applications interact with other apps via APIs, they leverage on the legacy systems and they grow in complexity from day to day in a nonlinear fashion. What does this mean for testers, particularly those working in software testing companies in Melbourne? How do testers leverage artificial intelligence to verify the ever growing suites of code? What would happen as AI works its way to production apps and how would testing change?

ARTIFICIAL INTELLIGENCE IN SOFTWARE TESTING HAS ARRIVED
With the AI arrival, machine learning could make a lot of software testing jobs faster, easier and more reliable. AI definitely is going to change the testing landscape and the jobs of testers in software testing companies in Australia. With continuous integration, continuous delivery and DevOps as the hot and trending topics in every software development talks these days, the pressure on testers has never been more intense. The solution then is AI in software testing. There are tons and tons of test data today and it’s very difficult for a single individual to get through it all. This is where artificial intelligence in software testing could come in and help.

THE AI BOTS
AI bots-based testing needs very little maintenance. Moreover, they are capable of discovering new paths via the product on their own. Testing software is just the right field to apply artificial intelligence as testing, whether it be manual or automated. This situation exists because testing integrates many human as well as machine-generated data. With organizations waking up to AI, developers are thinking of facilitating decision making, drive automation and boost efficiency in the field of testing.

FIVE WAYS AI WILL CHANGE TESTING
The following are ways that the introduction of Artificial Intelligence would change software testing.

1. Tools would change. Soon, the next generation of testers would laugh at the notion of choosing, managing and driving systems under test or SUT. Artificial intelligence would do it faster, cheaper and definitely better.

2. AI would become a testers BFF. The interactions of AI with the system multiplies results with manual testing. If test cases generation is not enough to be BFF with AI, now Infosys has an offering for AI-led quality assurance. The idea is that a system uses data in the existing QA systems, like resolutions, defects, source code repo, logging, test cases and others to help in identifying problem areas of a product. Soon there will be a trend wherein humans would have less and less dirty mechanical work to do with the implementation, execution and analyzing test results, but they would still be necessary part of the test process to approve and to act on findings. Already, this could be seen today in AI-based testing products.

3. Trash determinism. There would be an array of possible outcomes. A test engineer would have to run a test a lot of times and make sure that the conclusion is statistically correct. The test infrastructure would have to support learning expected test results from the same data, which trains the decision-making artificial intelligence. One of the best views to how testers would work with AI as software gets less deterministic is isolating the learning algorithms of the system from the system itself. Current data is isolated to expose how a system learns and what it concludes, based on the data provided.

4. Testes would be mystics. How does AI that test knows that a system tested is correct? Humans do this through finding a source of truth, a stakeholder, product owner, and a customer. Test engineers would have a different skills set to build and maintain AI-based test suites, which are AI-based products. The requirements of the job would include more focus on data science skills. Furthermore, test engineers would be required to comprehend and learn some deep learning principles.

5. Testers would become extinct. Testing eventually could be done better by an AI with enough training data. Perhaps there is hope in the length of runway between today and where artificial intelligence takes off. It’s easy to be stuck on a tester’s own importance. However, like the asteroid that slew dinosaurs, artificial intelligence is coming.

THE PROS AND CONS OF AI IN TESTING
Just like all things, artificial intelligence in software testing comes with its pros and cons. One of the biggest selling points of Ai is instant feedback mechanism. As manual testing a long way off in the evolution shit to DevOps and Agile, it simply is not achievable for manual testers to provide agile developers immediate assessment on how their constant alterations and inputs to the app affected the current user experience. Also, AI app could effectively help generate and optimize test instances, prioritizing testing and automation, boosting UI testing and minimizing tedious analysis jobs.
Although the pros of AI are manifold, it naturally has its share of critics for a looming possibility of jobs lost and lack of intervention due to judgment. The intervention of artificial intelligence in software testing would almost certainly would displace some from low-skilled jobs. Moreover, its intelligent adaptability would also leave less human involvement control to make judgment calls to exclude special cases.

As artificial intelligence finds its way into software and automation testing, companies till are contemplating whether to embrace it or not in their product engineering practices. Since people are good in exploration, creativity, analysis, understanding and application of knowledge, these are the areas that we would witness being catered by them, with the rest moving to artificial intelligence. By working harmoniously with AI in the future, human testers and automation testing jobs in Australia would see the most valued and interesting aspects of testing. Thus, the best way forward is for people and machines to exist simultaneously, leaning on the strengths of each other.
 

This article was contributed by RITESH MEHTA and was previously published here

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