Being a developer is more demanding than it has ever been. The time-consuming and error-prone repetitive activities that make up so much of software development may be frustrating. Many organizations can’t keep up with both increasingly complicated current code and the expanding market for new application development because talent is in limited supply, teams are overworked, and many businesses can’t keep up with both.
 
Speculating on how artificial intelligence may enhance software development is fascinating for AI enthusiasts. Will AI assist in the creation of prototypes in days rather than months or years? Will it help human programmers become better coders? Because AI research is so wide and computer programming is so flexible, it’s difficult to picture how software development will appear when intelligent programs can assist us interact with code.
 
But what many developers and tech managers don’t realise is that AI’s usefulness for development teams has made huge leaps in just the last several years. In fact, the early stages of AI-assisted software development are already here.

Automation Is Incomplete Without AI Assistance

It’s difficult to find a company that doesn’t strive for efficient, agile software development, and automation technology has made this a reality at scale. In the last decade, automatic test execution has improved software quality by allowing developers to receive instant feedback on their code modifications and react accordingly. Robot helpers produce pull requests in automated software pipelines, allowing for continuous delivery of changes.
 
Companies that have embraced technology, on the other hand, are discovering that automation alone isn’t always enough. Bottlenecks still exist in automated processes, the majority of which revolve on the development of new code. Automating the execution of hundreds or thousands of unit tests, for example, may be done rapidly, but writing the tests takes hours or weeks for the development team. Automated pipelines encourage garbage since they lack tests to validate changes. As new code (and new tests) are introduced, what would otherwise be an automated process is disrupted by the requirement for continuing manual work.
In this manner, AI can begin to break down the trade-offs that developers and IT administrators face when it comes to time, cost, and quality of work. Developers may use AI-assisted development to produce new products faster and more cost-effectively without sacrificing quality. Developers may return to the creative activities that drew them to their employment in the first place after repetitive chores are done consistently and quickly.
 

The Incredible Efficiency of AI

AI-assisted software development is already in use in several areas that place a high priority on code quality, such as banking. For example, Goldman Sachs recently used AI for coding to increase the productivity of their software development. They developed a full test suite in hours by using an AI tool to write over three thousand unit tests for a legacy application with fifteen thousand lines of code. When compared to manually creating each unit test, which took an average of 30 minutes, the AI tool was able to generate tests 180 times faster. Over the course of the project, the bank was able to save over a year of development time.
 

What’s Next?

Investments in AI for software development will become increasingly prevalent across sectors as AI technology advances and solutions for additional use cases are created. It won’t be long before incorporating a new level of efficiency-boosting technologies into the development process becomes a must in order to remain competitive and scale. But, in the meanwhile, the first prototypes of AI-assisted software creation are now available, providing a taste of what the future of coding may hold.
 
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