In this paper we describe a system to augment human project managers of large scale software projects by predicting future SLA violations, using a machine learning approach. We detail the way we collected, processed, and formatted the data from a much larger database ill-suited for the problem at hand. We then detail how we extracted training examples through prediction engineering, as well as our semi-automated feature engineering approach. Our system was able to achieve a false positive rate of .311, compared to a non-machine learning baseline of and a simple machine learning baseline without feature engineering scoring .689 and .723 respectively...
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