AI in the Application Testing Process

Article

Pablo Ramirez

Project Manager

What is AI?

In theory, the term artificial intelligence (AI) 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.

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 recognize 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 movie “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 information, such as smart assistants, facial recognition, the GPS integrated in our devices, all this information is available today through of our smartphones, which is processed quickly and through specific artificial intelligence algorithms, help us every day, many times without realizing it.

On the other hand, large retail companies 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 are going to compare fit you, without having to go to a tester. The financial sector, such as banks, have 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 realize that they are being served by a BOT.

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

AI in Testing

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

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 in recent years, organizations are forced to face digital challenges, which is an outstanding asset for the company to survive. 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 ensuring 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 and processes with intelligent quality engineering capabilities. While only time will tell what extent test AI systems become a reality, it is clear that significant efficiency and speed can be achieved by applying these smart technologies, driving growth and improving results across industries.

Therefore, while there are high expectations and some evidence of the application of supervised learning as a core part of machine learning (ML) to make quality engineering (QE) smarter, the adoption and application of These methodologies have not yet reached the maturity required to show visible 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 to 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 not only helps to perform an in-depth intelligent what-if analysis, but also helps to predict future quality and determine the necessary plans in development activities and proof. This helps improve the test by incorporating actual usage patterns into the test in a smart way, and it also supports methods such as 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).

In summary, for organizations 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 organization to acquire these skills.

The role of testers is not threatened by the development of this technology, on the contrary, it will be favored 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 training 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, therefore we need humans for all this.

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. The human being is the user of the applications and technological innovations that are created, and it must be considered that understanding, creativity and the human context are necessary characteristics to ensure high quality products. In other words, manual testing remains essential and 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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