Abstract
If you regularly work with open-source code or produce software for a large organization, you're already familiar with many of the challenges posed by collaborative programming at scale. Some of the most vexing of these tend to surface as a consequence of the many independent alterations inevitably made to code, which, unsurprisingly, can lead to updates that don't synchronize. Difficult merges are nothing new, of course, but the scale of the problem has gotten much worse. This is what led a group of researchers at MSR (Microsoft Research) to take on the task of complicated merges as a grand program-repair challenge, one they believed might be addressed at least in part by machine learning.
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