Using Big Data to Improve Product Quality

Feb 18 2014 (6:00 PM - 8:30 PM)

Using Big Data to Improve Product Quality
In the past, most of the software life cycle happened before launch. Few products would send crash reports or track consumer usage after they shipped. Companies eventually realized that in-house testing could not predict actual user behavior. Therefore, as storage, CPU, and bandwidth increased, so did the collection and processing of user data. Nowadays, Agile and Devops have blurred the boundaries between development, QA, and operations. How can today’s teams leverage user data to continuously improve their product? This talk focuses on the tools and methods used to collect, analyze, and learn from user data in order to increase product quality and customer satisfaction.

About our Speakers:: Thomas Debeauvais
Thomas Debeauvais is a PhD candidate in Informatics at UC Irvine working with Professor Cristina Lopes, one of the creators of aspect-oriented programming. Thomas received his MS in computer engineering from Bordeaux in France. His current research uses techniques from statistics, data mining, and machine learning to analyze player behavior in video games. He has prototyped a medical web service for Siemens Corporate Research, and mined player behavior for Xerox PARC and Microsoft Research. Beyond SQL, Python, and R, he plays badminton and watches an awful lot of movies.

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