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Using Code Smells and Profiling to Help End Users Detect and Fix Performance Problems in Graphical Languages
End user programmers often struggle to create programs that run quickly and effectively, which can be a major deterrent in completing their tasks as desired. Current research has primarily focused on catching user mistakes, such as errors or misused formulas. However, end users deal with issues other than just correctness. In particular, there are very few tools and very little research aimed at helping end user programmers detect and fix performance issues. This proposal will detail three specific methods: detecting code smells, combining code smells with profiling information, and the automatic refactoring of code smells. These methods will be evaluated through several user studies to ensure that they are helpful.

Major Advisor: Chris Scaffidi
Committee: Margaret Burnett
Committee: Carlos Jensen
Committee: Alex Groce
GCR: Hans Luh 

• Electrical Engineering and Computer Science*