Consider the premise of Big Data:
better conclusions = same algorithms + more data + more cpu
If this were always true, then there would be no role for human analysts
that reflected over the domain to offer insights that produce better solutions
(since all such insight is now automatically generated from the CPUs).
This talk proposes a marriage of sorts between Big Data and software
engineering. It reviews over a decade of work by the author in exploring
user goals using CPU-intensive methods. It will be shown that analyst-insight was
useful from building “better" tools (where “better” means generate
more succinct recommendations, runs faster, scales to much larger problems).
The conclusion will be that in the age of big data, human analysis is still
useful and necessary. But a new kind of software engineering analyst is required- one
that know how to take full advantage of the power of Big Data.
ABOUT THE AUTHOR:
Tim Menzies (P.hD., UNSW) is a Professor in CS at WVU; the author of
over 230 referred publications; and is one of the 50 most cited
authors in software engineering (out of 50,000+ researchers, see
http://goo.gl/wqpQl). At WVU, he has been a lead researcher on
projects for NSF, NIJ, DoD, NASA, USDA, as well as joint research work
with private companies. He teaches data mining and artificial
intelligence and programming languages.
Prof. Menzies is the co-founder of the PROMISE conference series
devoted to reproducible experiments in software engineering (see
http://promisedata.googlecode.com). He is an associate editor of IEEE
Transactions on Software Engineering, Empirical Software Engineering
and the Automated Software Engineering Journal. In 2012, he served as
co-chair of the program committee for the IEEE Automated Software
Engineering conference. In 2015, he will serve as co-chair for the
ICSE'15 NIER track. For more information, see his web site
http://menzies.us or his vita at http://goo.gl/8eNhY or his list of
pubs at http://goo.gl/0SWJ2p.