A guest post from Artur Meyster, founder of Career Karma.
The tech industry has boomed in the last few decades. Knowing this, many companies have started to invest heavily in new technologies. With humans always craving for more, large companies like Google have invested in Artificial Intelligences (AI) and machine learning.
AI is changing the world as we see it. This technology has changed the way companies build their products and how they provide services. For example, years ago, using AI-powered email services was unthinkable. Now the technology is real, and it’s changing how users can write and send emails.
It is vital to point out that AI is helping humans accomplish several tasks by making them less time-consuming. AI and machine learning are not only affecting industries but also changing how developers code. In this article, we discuss how AI is likely to change programming within the next decade.
Automatic Code Generation
AI hasn’t just helped organizations automate manufacturing processes; it is also helping developers code. Writing code is labor-intensive and time-consuming. For that reason, AI-powered assistants have been implemented in coding. They are capable of making suggestions during the programming process. As a result, the coding process is more comfortable.
AI is changing programming, and there’s no way back. Imagine a Web Developer that needs to build a website within a week. The coding process would be more relaxed thanks to AI.
Also, within the next decade, systems could probably ‘translate’ ideas into actual code. For example, imagine explaining to a machine your project ideas, and the system converting them into executable code. It sounds surreal, but the truth is that AI is changing reality by making science fiction ideas come to life.
AI Testing Assistance
The testing phase is crucial during the development process. Without it, developers can’t ensure the quality of the product. Testing an application or software can be time-consuming. For that reason, experts have created AI tools that can be used during testing.
When Mobile Developers build applications, they have to run the same test several times to ensure the app is error-free. In this case, AI is helpful as it creates specific cases and performs regression testing. As a result, within the next decade, the testing phase will become less time-consuming and costly.
Enhanced Deployment Control
The deployment stage concerns not only the release process but also upgrading and updating. Developers must ensure that future app enhancements increase the app’s performance and stability.
AI technologies increase the efficiency of deployment control activities, helping developers release upgrades successfully. In the future, AI will reduce the chances of failure during deployment.
Easier Bug Identification Process.
During the development process, programmers have to deal with bugs and their identification. Most companies hire QA Engineers to identify and report bugs. These professionals rely on AI tools to make the process of finding bugs easier.
AI is changing not only how developers code but also how code is examined. This is improving the process of coding. Within the next decade, programmers will be able to develop higher-quality solutions, while spending even less time coding.
Accurate Estimates
Every developer has to deal with deadlines and budgets. As programming is a time-consuming task, meeting customers’ requirements can be challenging. In the same way, accurate estimates require context understanding and expertise. For that reason, tech pros have implemented AI to enhance their estimates and make them as precise as possible.
AI and machine learning have been used to make predictions on the time and money it takes to complete a project. These technologies use predictive analytics to offer better solutions that meet customers’ demands.
Conclusion
AI is changing not only how people code but also how they maintain that code. For that reason, AI will change and improve programming in the next decade.
Future developers will be able to spend less time on repetitive tasks like testing and finding bugs. In short, future solutions will be more sophisticated as programmers can focus on more important tasks.
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