Intelligent Software Development, Courtesy of Intelligent Software

Stephen Frein

The machine learning age is well underway. Today’s software can see novel patterns that humans are unable to see and improve task performance based on experience. Learning algorithms are widely used for varied purposes, including loan approval, intrusion detection, fraud prevention, risk analysis, and online sales optimization. Yet, like the proverbial cobbler who left his children shoeless, software practitioners have been slow to apply the benefits of machine learning to their own work. Join Stephen Frein for a tour of the current machine learning landscape and its most popular tools and techniques, and see how these can be applied to the practice of software development. Through intelligent, machine-driven analysis of existing data sources, we can predict defects, forecast effort, improve design, and streamline testing. Such efforts won’t be push-button easy and will be far from perfect, but they are currently achievable and probably valuable. Best of all, we can start small with a few simple experiments and without the encumbrances that commonly attend “big data” projects. We all want to build software more intelligently, and now intelligent software is in a position to help.

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