Software Testing Trends: The Digital Industrialization Moves Forward
As technology continues to rapidly evolve, staying informed and up-to-date is crucial for everyone. The software industry has become the epicenter of this digital transformation, an industrialization process that has already begun.
The news of these changes is crucial for every person who works in software, as well as every person who works in a technological field. They should follow this for the impact that software has on everything. Moreover, anyone who is interested in the future, especially since technology and production are the core of our society. And what is the most sensitive area for changes in software development right now? Testing. It has drastically changed with the new advances and is leading the way.
With this purpose, we have put together a bird’s-eye view of the top trends in software testing. For years, the most trusted methodologies in this field have been Agile and DevOps. They assure the speed that the market demands and the possibility to adjust to novel tools and requirements. But what is trending right now? Let’s explore the blueprint of this new industrial revolution by understanding what software testers are doing differently.
1. AI Testing
Impressive code-generation tools like GitHub Copilot started an AI implementation wave among developers. The latest innovations have increased exponentially what a person can do in a workday, but Large Language Models have brought to the table some amazing testing capabilities.
Errors in software code cost U.S. businesses $2.08 trillion in 2020 alone, assures CodiumAI, a testing tools company. Last week, we published a list of some of the most important AI Testing tools. In there, you can find examples of the impact this is having on the software market. From self-healing tests to visual locators and AI analytics, AI-based automation testing offers immediate and impactful opportunities for growth and innovation.
However, this doesn’t mean that manual testers are obsolete, quite the opposite. AI-powered testing tools can help with test execution by automating repetitive tasks, helping manual testers to focus on more complex testing scenarios that require human intervention and creativity. These tools can assist in identifying defects, analyzing test results, and predicting potential issues before they occur. This will allow humans to focus more on exploratory testing. With the help of AI, manual testers can enhance their capabilities and reduce the time spent on repetitive tasks, thereby increasing efficiency and productivity.
2. Fake Data
Testers know the importance of realistic data in creating thorough and complex testing environments. However, manually generating that data can be time-consuming and error-prone. That’s where fake data, a crucial tool for advancing software testing, comes in.
Fake data enables testers to simulate a wide range of scenarios and edge cases that may not be present in real-world data. This saves time and resources while ensuring the accuracy and consistency of the data used in testing. Moreover, fake data tools can help protect sensitive or private information during testing by using data that closely resembles the real thing without compromising privacy or security. With several popular fake data tools available for software testing such as Gretel, Mockaroo, DataGenerator, and Tonic, software developers and testers can save time, resources, while helping maintain privacy and security.
As AI and machine learning continue to grow in software development, the importance of accurate and diverse data for training and testing models will only increase. Fake data will continue to develop and be recognized as an indispensable tool for software development and quality assurance in the upcoming years.
3. Environment Simulation
Everyday, more and more devices are becoming “smart”. The Internet Of Things is a market that has been growing for many years now. As we said in a previous article, this growth is going to demand more complex ways of people to interact with machines, and this means that software will have to adapt to an unpredictable number of different devices. Better than trial and error is the ability to produce software that can adjust to almost any scenario possible.
Along with an increased demand for data, this means that testing will have to cover different environments, or we can say, different stances of the Natural Environment that humans are used to navigate. We will see this in new cloud-based tools for simulating these scenarios. The computational power needed to produce this will for sure need AI technology and communication advances, like for example the ability of cloning entire ecosystems that companies like NVidia are advertising with now.
More and complex data, and the simulation of how these interact with the material world will be a must have for almost any software production company.
