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Can Artificial Intelligence and Machine Learning Take Over Software Development?

Artificial intelligence and machine learning continue to grow as trends within the tech industry and evolve with new applications in a wide variety of new business sectors. This popularity and constant discovery of new use cases have perpetuated the idea that the “robots” will eventually take over the world or, at least, take over jobs from actual working humans who require a salary.

Although AI can already paint pictures, help in medical treatments, drive cars, and everything in between, where does it stand in the world of software development? Are human developers and nearshore development companies a thing of the past already?

The short answer is: no, not yet at least. While both machine learning and artificial intelligence have already made their marks in the world of software development, their full potential isn’t yet used within the industry. That’s not to say that these technologies won’t replace outdated practices and, yes, maybe even jobs in the future – but not quite yet.

To understand where AI and machine learning could possibly go, it’s important to first grasp where these techs’ functionalities and capabilities have already been useful within the software development process.

How Do AI and ML Already Drive the Software Development Industry?

Machine learning and artificial intelligence are already helping software developers and the DevOps process meet the rapidly growing demands of the market in several ways. These technologies help predict, automate, and make changes that may take humans quite a bit of time to take care of otherwise. Some of the ways they do so include:

  • Creation of Smart Code – Creating code is a tedious and rather difficult task even for the most experienced and seasoned developers. Machine learning and artificial intelligence have brought many “smart tools” to the development table to help alleviate the work and stress in this process for human developers.

These AI tools provide coding support, low-code or no-code development, corrective recommendations, and drag and drop code support, and even samples to help make developers’ jobs a bit easier. These tools then extend into the world of automated code review, analysis, and refactoring. This not only raises the overall quality of the written code but also helps enforce code quality and security policies.

Machine learning and artificial intelligence also cover automated testing of the already developed code via analytical review against common errors and anti-patterns that busy devs may accidentally look over. This includes factors such as concurrency race conditions, resource leaks, and underutilized computing units.

  • Product Re-Alignment, Rapid Marketing Targeting, and Re-Targeting – As the market demand continues to rapidly shift at an unbelievable rate, the classic prototyping methods for new products and field testing aren’t really available for devs. In order to keep up with the change in speed that occurs before a product is even released to the public, automated processes powered by artificial intelligence and machine learning help in reducing timelines.

Machine learning and artificial intelligence have already reduced the amount of time it takes to conceptualize, produce, and eventually deploy a software product to the public. They then go a step further to act in the live tracing of analytics across the market trend movements.

This helps give an insight into market trend movements while also helping to make smart predictions about future customer demands. As these techs continue to work, they also get more accurate, as they learn from their previous predictions to improve recommendations.

  • Infrastructure and Data Management – Machine learning can accurately predict data storage locations or if said data actually exists to outperform more traditional database indexes. It’s not only significantly faster for indexing within databases, but also requires much less memory.

Data engineers already use machine learning to manage their databases where it enables quicker response times than humans while also tuning databases for performance. ML possesses the ability to simplify problems by managing the many database configurations that affect its overall working behavior. This streamlines processes for developers and engineers while preventing future problems.

What Does the Future of Software Development Look Like With ML and AI?

Although Hollywood has done a good job of showing just how close we are to the start of the machine overlord revolution, the world of software development isn’t quite there yet in terms of the applications of machine learning and artificial intelligence. As of right now, these technologies have only intensified the need for even more skilled developers to work alongside the “robots.”

The question for the future is what kind of software engineers will the industry require once ML and AI use cases expand even further. New roles will inevitably arise within the development process to work alongside the tech. Those working within the world of software development can definitely expect to see machine learning and artificial intelligence expand while adopting more tools to help make their jobs much more efficient and effective.

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