11 research outputs found
Crowdsourcing the State of the Art(ifacts)
In any field, finding the "leading edge" of research is an on-going
challenge. Researchers cannot appease reviewers and educators cannot teach to
the leading edge of their field if no one agrees on what is the
state-of-the-art.
Using a novel crowdsourced "reuse graph" approach, we propose here a new
method to learn this state-of-the-art. Our reuse graphs are less effort to
build and verify than other community monitoring methods (e.g. artifact tracks
or citation-based searches). Based on a study of 170 papers from software
engineering (SE) conferences in 2020, we have found over 1,600 instances of
reuse; i.e., reuse is rampant in SE research. Prior pessimism about a lack of
reuse in SE research may have been a result of using the wrong methods to
measure the wrong things.Comment: Submitted to Communications AC
Beginning with machine learning: a comprehensive primer
This is a primer on machine learning for beginners. Certainly, there are plenty of excellent books on the subject, providing detailed explanations of many algorithms. The intent of this primer is not to outdo these texts in rigor; rather, to provide an introduction to the subject that is accessible, yet covers all the mathematical details, and provides implementations of most algorithms in Python. We feel this provides a well-rounded understanding of each algorithm: only by writing the code and seeing the math applied, and visually inspecting the algorithm’s working, will a reader be fully able to connect all the dots. The style of the primer is largely conversational, and avoids too much formal jargon. We will certainly introduce all required technical terms, but while explaining an algorithm, we will use simple English and avoid unnecessarily formalisms. We hope this proves useful for individuals willing to seriously study the subject