Rice University leads $11M effort in big data software analytics

Program synthesis: There has been a lot of interest in the formal methods community to automatically generate programs (for small instances) with the target specification coming from input-output examples (e.g., Excel Flash Fill [3]), program templates or holes (called Sketches [4]), reactive models of adversarial environments, formal invariants etc. Also the solution techniques used vary considerably: from game theoretic solving, SAT solvers, model checkers, to version-space algebras and others. The community has not yet fixated on a specification language, or a solving technology. The industrial nature of the tools being leveraged (e.g., model checkers and SAT solvers from the hardware community) gives hope for promising developments. A Berkeley course [5] covers a good spectrum of the current developments.

If I were to guess, maybe the Rice researchers are approaching the code completion/correction problem as mining for fragments of large codebases that are incomplete/incorrect and applying program synthesis to fill those fragments. Of course that would mean that they would also need to mine the specification requirements for those fragments. All of this is easier said than done, and it would be an ambitious project. Swarat has also done some really cool work on "probabilistic reasoning for programs" and "verification of probabilistic programs", so that might be part of it too. (Of course, I may be completely off-base! After all, we are commenting on a non-technical funding announcement here.)

[1] Swarat's publications: http://www.cs.rice.edu/~sc40/pubs/

[2] Moshe's publications: http://www.cs.rice.edu/~vardi/papers/index.html

[3] Excel's FlashFill from Sumit Gulwani, researcher@MSR: http://research.microsoft.com/en-us/um/people/sumitg/flashfi...

[4] The Sketch program synthesizer: https://bitbucket.org/gatoatigrado/sketch-frontend/wiki/Home

[5] Ras Bodik/Emina Torlak: Berkeley course material on Program Synthesis: http://www.cs.berkeley.edu/~bodik/cs294fa12

This, absolutely. Big data, data mining, and machine learning are really cool topics but the words became overused, overhyped, used out of context, especially by people who don't really understand what these are.

I have an old co-worker who spent a lot of time working with large excel spreadhseets, using some formulas, sorting it to look for things, etc.

He lists "data mining" on his linkedin, and has a ton of people who endorsed him for it.

This became a little off topic, but I hate how venture capital, grant funds, the media, and misinformed people completely butcher these topics.