Using Machine Learning to Develop Better Software Systems

February 1, 2019Uncategorized

Machine learning may sound like it continues to belong exclusively in the toolkits of science fiction authors, but that is not the case. In fact, computers have long been employed to automate repetitive or tedious tasks such as making extended calculations that, while simple in technique, would take too long for humans to complete. With this in mind, it should not be much of a stretch to realize that machine learning will soon be establishing its domain in software development as well. 


For decades, developers have been harnessing the power of automated tools. Thanks to compilers, programmers can easily and quickly compose machine code. Scripting languages make it possible to combine more complex programs. In general, the development, management and roll-out of software is made much simpler by numerous automated processes such as software testing and deployment tools and container orchestration systems. It stands to reason that machine language is the next innovation to come down the pike to make software development more efficient and task-specific. 


Machine learning involves more than millions of lines of numbers and letters. In fact, it is designed to adapt as conditions change, getting “smarter” with every piece of feedback it receives. The software of the future will do more than simply run mechanically through a pre-coded set of instructions. It will instead collect, curate and analyze data, using this information to modify itself and become more accurate and efficient. 


Although the jury is still out, early indications suggest that machine learning can give an added nimbleness to software development. The new flexibility may come from an increased ability to predict future needs while operating faster and requiring less memory. Because machine learning enables engineers to deploy infrastructure more efficiently and continue to monitor it for security breaches and outages, it seems more than likely that this technology will be studied, improved and used increasingly by developers and engineers alike. 

Popular literature contains an abundance of stories in which automated systems become so powerful that they take over and enslave the humans who originally invented them. Although it is far from likely that this will ever occur, the future of machine learning in software development appears bright and nearly boundless. Innovators such as the professionals at will doubtless be at the forefront as they create customer-centered, feature-rich multi-platform packages for a wide array of businesses. Far from being a frightening prospect, this innovation seems poised to make human beings even more important and productive than ever.