Machine learning (ML) is an advanced technology that’s transforming the software development landscape. As a professional in the field, you’ve probably noticed that not only are development processes more adaptive, efficient, and user-centric today than ever before, but this evolution happened at a rapid pace. With Artificial Intelligence dominating the tech sphere, machine learning has, naturally, come to influence everything from coding practices to the end-user experience.
As we move into the future of the industry, these technologies are set to become ever more sophisticated. In this article we’ll be taking a look at how machine learning has reshaped software development and suggesting how it could impact its future.
Transforming Code Quality
In software development, high-quality code is an essential part of the process. However, it’s often time-consuming. Machine learning offers the perfect solution, with algorithmic programs assisting everything from automating code review to detecting bugs and even suggesting improvements.
The likes of Codacy, DeepCode and CodeGuru have become important items in a developers toolbox, and they all employ ML models trained on vast code repositories— which help them identify coding patterns and isolate potential issues.
Machine learning also has plenty of utility as a means of detecting early defects in code segments. Predictive capabilities allow it to analyze historical data and patterns in the codebase, highlighting potential issues before they have a chance to occur. This means that teams can then focus their efforts on high-risk areas, optimizing the quality assurance process.
Revamped Automating Testing Processes
Software testing is another element that has seen significant improvements thanks to machine learning. Traditional testing methods require extensive manual efforts, but ML-based systems can automate the entire lifecycle (test case generation-execution-result analysis). Not only is this speeding up the process, it also improves test coverage and accuracy.
Tools like Testim.io and Applitools use machine learning to detect changes in the user interface and adapt test scripts accordingly. Rather than placing the onus on engineers to manually update tests for every small change, ML models can identify if a change is intentional or if it’s the result of a bug or code failure. This adaptive approach minimizes the time spent on maintenance and allows for continuous integration and deployment pipelines to run more smoothly.
Real-World Applications
Of course, software development is far from an isolated industry and doesn’t exist to serve itself. The digital ecosystem is dependent on efficient software development, not least of which is the online gaming industry.
As is to be expected in the digital age, gaming is a complex and immersive activity, which places a heavy burden on the shoulders of designers and developers. Players today expect premium quality gameplay, smooth mechanics, hyper-real graphics—and that’s just those on consoles and desktops. Virtual Reality games development is at another level!
So, there are clearly use cases for machine learning in the creation of new gaming experiences, but its utility goes far beyond that. Operators and studios across the board, from conventional RPGs to more exotic sectors like online casino gaming, are waking up to the potential of implementing ML directly into their online gaming environments.
Let’s stick with online casino gaming, for the moment. Now, iGaming users are often faced with an overwhelming choice of games to play. A platform like PokerStars Casino is stocked to the brim with casino classics, from table games like blackjack to progressive jackpot slots. It’s unlikely a player will be able to try them all in a single gaming session, so operators can use ML to tailor game suggestions based on player behaviors and preferences.
Not only does this streamline the user journey, it also aids the operator in achieving their customer retention targets.
What about in conventional gaming? Well, an AI-powered learning model might not be too much to imagine in the years to come. With machine learning models underpinning such online assistants, developers might soon be able to provide in-game support that understands and analyzes player patterns to offer tips or highlight strategies to improve their gameplay.
The Future of Software Development
Machine learning’s influence on software development is only set to grow, making it a crucial area for developers to understand and leverage. As the decade progresses, we predict that ML will soon be facilitating the seamless adaptation of code across different platforms, automatically adjusting for platform-specific nuances in a way that minimizes the need for manual involvement.
Moreover, advanced algorithms and processes could significantly improve low-code and no-code development, allowing even the average user to build complex applications in an intuitive fashion.
