Artificial intelligence in the application testing process

What is Artificial Intelligence? In theory, the term refers to the intelligence exhibited by machines. However, the term is applied when a machine mimics the cognitive functions associated with human capabilities, for example: learning and problem-solving. Artificial intelligence in application testing can be a crucial tool.

A world without borders

Today we live in a world where almost nothing can surprise us, the space between reality and science fiction is very narrow. However, we sometimes encounter situations where we cannot recognise when we are interacting with humans or robots. The advancement of artificial intelligence (AI) has occupied an important place in our daily lives and has become the key to the fourth industrial revolution.

Many of us must remember the HAL 9000 computer in the 2001 film “A Space Odyssey,” which demonstrated what artificial intelligence can do for humans. For many people, the beginning of this phase began with the arrival of smartphones in 2007, which allowed everyone to use smart assistants, facial recognition, and GPS.

Although, large retailers are beginning to use AI to provide the customer with a better shopping experience. Such as mirrors that allow you to virtually see how the clothes you’re interested in fit you, without having to try them on.

The financial sector

The financial sector has integrated smart ATMs, which allow practically all the operations that were previously carried out in a box or in customer service. Large hotel chains today use BOTs based on intelligent IVR to schedule their guests. Thanks to the great advance that AI has today for this area, it is very difficult to realise that they are being served by a BOT.

Many organisations are forced to find a balance in cost versus benefit, in achieving a quick return on their commercialisation process and in turn delivering a good experience to the end user. The current goal of organisations is to run more tests, find incidents quickly, and release products more quickly. Artificial Intelligence can help achieve this goal.

Artificial intelligence in application testing

Advances in automation and artificial intelligence have paved the way for real-life solutions that can help organisations save money and resources. For its part, intelligent automation can further help organisations by using existing data and automatic analysis based on that data. Ultimately, this helps to improve operations and workflows.

The biggest challenge in application testing is having enough time to test and develop the correct test methods and procedures.

Faced with situations such as those experienced by the pandemic, organisations are forced to face digital challenges. Is it possible to produce high-quality digital assets such as e-commerce, supply chain systems or engineering and management solutions, without spending a lot of time and money on their quality?

In other words, can you test a system without testing it? That may seem like an impossible dream, but the industry has already started talking about developing systems. While only time will tell what extent test AI systems become a reality, it’s clear that significant efficiency and speed can be achieved by applying these smart technologies.

Therefore, while there are high expectations of supervised learning as a core part of machine learning (ML), to make quality engineering (QE) smarter, adoption of these methodologies have not yet reached the numbers need to show results.

Use cases

The benefit of all this is that some companies are now working to change traditional models and are leading the way in applying artificial intelligence. This is for QE for unsupervised models, natural language processing (NLP), and computer vision technology.

We have witnessed the emergence of new use cases for this type of test. For example, real-time analysis of production events and application logs. This not only helps to perform an in-depth intelligent what-if analysis, but also helps to predict future quality. Therefore, revealing the necessary plans in development activities and proof.

This helps improve the test by incorporating actual usage patterns in a smart way, and it supports methods like the left shift test.

Another use case that seems to have gained ground is the use of AI for the generation and management of test data. For example, we can use this type of test to identify coverage gaps, compared to real user experience patterns.

The same can also be applied successfully in the creation of synthetic data, for example, to comply with the rules of handling of personal data (GDPR).

For organisations to reap the greatest benefits from AI in quality engineering, they will need their teams to strengthen their knowledge and experience of the tools, the overall QA and IT strategy, and the business objectives of the company in your set. It is a great opportunity, not only for companies, but also for QA people.

QA teams should have QA engineers with skills in data science, analytics, and artificial intelligence. If necessary, they should collaborate with other parts of the organisation to acquire these skills.

Testers

The role of testers is not threatened by the development of this technology, on the contrary, it will be favoured. Since AI requires constant interaction of human testers with them. Another important point, to train artificial intelligence, we need good input / output combinations (which we call a training data set).

So to work with modern software, we must choose this training dataset carefully, as artificial intelligence begins to learn from this and begins to create relationships based on what we deliver to it. Also, it is important to monitor how the AI ​​is learning, this will also be vital to know how the software will be tested.

Human participation in artificial intelligence in application testing is still necessary. Last but not least is to make sure that when working with artificial intelligence, the ethical, security and privacy aspects of the software are not compromised.

High expectations about the benefits of artificial intelligence

The latest World Quality Report 2020-2021 highlights that a large part of those surveyed are excited about the possibilities that artificial intelligence offers. Almost 90% say that AI tests and AI tests are the largest growth areas within their companies. And 80% stated their intention to increase the number of AI-based trials and proofs of concept.

Conclusion

Although artificial Intelligence continues to advance, the truth is that it is not easy to imitate the human brain. People are using the applications, and it must be considered that understanding, creativity and the human context are necessary to ensure high quality products.

In other words, manual testing remains essential. Automation and artificial intelligence must complement each other. They are completely different functions and should be used according to their respective advantages, rather than being compared.

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