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Artificial intelligence is currently being used in an array of sectors, from processing natural language to analyzing facial expressions, and more. It has become more of a buzzword in recent years. However, it’s also important to understand that the capability of artificial intelligence is constantly evolving. For example, Alexa and Siri were once considered state-of-the-art, but they’re now considered taken for granted. The same thing happened in software testing domain also. Innovations in automation have transformed the testing landscape. Despite the fear that “robots taking our jobs”, software testing professionals have always wanted automation in everything and there’re some real benefits they can gain by implementing artificial intelligence. Let’s have a look at how technology can transform software testing.

Smart testing

Though the testing phase plays a crucial role in SDLC, it’s often time-consuming, impractical and expensive, apart from maintenance forming one of the biggest costs. With implementation of artificial intelligence, testers can overcome this and put their best foot forward. This approach to testing can eliminate headaches for hand-crafted testing. By merging analytics and machine learning to facilitate decision making, testers can unlock the patterns, drive automation and finally improve testing efficiencies.

Smarter end product

Nothing can beat a smarter and more productive end product and artificial intelligence algorithms can be extremely helpful for testers to attain that. Once it’s deciphered how to intelligently use AI and the areas within the process where AI can be implemented are identified, the entire process can be facilitated and testers would be able to find the maximum number of bugs. Thereafter, the result can be handed over to the developers to refine the product and make it more productive.

In test automation

In test automation, artificial intelligence can be implemented for object application categorizations for user interfaces quite efficiently. Upon observing the controls’ hierarchy, testers can develop AI-enabled technical maps which look at the GUI (graphical user interface) and obtain the labels for various controls easily.

In exploratory testing

When it comes to conducting exploratory testing within a testing suite, AI can be effectively deployed. It can help testers to assign, monitor and categorize risk preferences. The right heat maps can be created by the testers to identify bottlenecks in different processes and increase test accuracy.

Increased test coverage

With the implementation of AI, overall test coverage and the depth and scope of the tests can be increased. There’re lots of different areas where it can be implemented – from test suite optimization, defect analysis and rapid impact analysis to traceability and UI testing, among others. Since a huge percentage of tests are repetitive, AI can take care of those and let the testers focus on the more creative side of testing, and to expand overall test coverage.

Eliminated business risks

With AI at work, business risks triggered by operational inefficiency can be hugely eliminated. AI bots-based testing can adapt and work around all the new features and paths through the product. These bots are low in maintenance and their algorithms are capable of learning from test assets to deliver intelligent hotspots like defect hotspots, application stability, failure patterns as well as failure predictions. These insights greatly help to automate, anticipate and promote decision-making capabilities, helping in developing quality early on in a project lifecycle.

Greater accuracy

AI bots aren’t hard-code and thus, they’re capable of discovering new paths and features automatically without breaking down. When such a bot encounters alterations, it analyzes them to determine if they’re new bugs or features. And as they’re open to change, they’re capable of cracking hard-coded test scripts. Manual testing may fail to observe changes, but AI-enabled bots notice every little change which is brought about. With this bot-based testing, testers can evaluate all changes and arrange them accordingly.

Important things to remember

With artificial intelligence finding its way into software testing, various companies are a bit hesitant about whether or not to embrace the technology. Unquestionably, humans are good at exploration, creativity, analysis, understanding, and application of knowledge, there’re other areas also that can be moved to AI.

A good approach can be retraining humans while observing AI bots and their results. In the future, AI will be able to help not just the testers working to come up with top-quality products, but every role involved in software development. So, human testers shouldn’t need to worry about that. Instead, they’d only need to start thinking a little differently. However, it’s important to maintain a balance. There’s a gut instinct which can’t be replicated via algorithms, making humans a crucial part of the process. While human-powered testing is crucial for the creativity it introduces to the process of finding bugs, most parts of testing are repetitive.

Final takeaway

Probably the final objective of using artificial intelligence in testing is to aim for a future where software enabled by this technology will be able to diagnose, test and self-correct. This can enable the testing team to reduce the testing time even further. We already have seen that the use of artificial intelligence in software testing can save money, time, and resources, and let the testers focus on only one thing – release a great product. Keeping in mind all the advantages of implementing artificial intelligence, it can be expected that human testers will be able to experience the most valued and interesting aspects of testing by working in harmony with artificial intelligence. So, the best way should be humans and machines existing simultaneously.