There is an old joke where two scientists are staring at a blackboard. In between the equations on the left and the equations on the right, it is written "and then a miracle occurs". The older scientist points to this missing link and comments "I think you need to be more explicit in step two"

 If you read the empirical SE literature, you can see a similar "missing link" in recent descriptions of how to conduct empirical SE studies. In one paper I just read, there was:

• 9 pages: on selecting methods

• 3 pages: on research questions

• 2 pages: on different forms of "empirical truth"

• 2 pages: on empirical validity

• 1 page: on the role of theory building

• 1 page: on data collection techniques

• And 0 pages: on data analysis (and then a miracle happens)

 Speaking as a data mining researcher, I'm asserting that selecting data analysis methods deserves more than 0 pages. Data analysis is not a simple matter of loading up a box with data, then pressing a button to produce the output.  The actual procedure is far more complex and, if done correctly, can lead to radical new insights that drive the next generation of the theorizing and experimentation. This talk will illustrate this process with detailed examples from effort estimation and defect prediction.

Biography:

Dr. Tim Menzies has been working on advanced modeling and software engineering since 1986. He is internationally recognised for his contributions to software quality assurance and prediction. He is the author of over 180 refereed papers, including 30 international journal articles and 17 book chapters and editorials. He has served on the editorial board of international journals, and on the PC of many numerous international conferences.
 
Dr. Menzies is now an associate professor at the West Virginia University's Lane Department of Computer Science and Electrical Engineering. Previously he was Software Engineer Research Chair, NASA IV&V. In that role, he monitored and assessed NASA research projects and proposals, and taught graduate software engineering courses. Also in that role, he introduced research quality indicators for NASA programs.