The Role and Relevance of Experimentation in Informatics

Informatics is a relatively young eld within sci- ence and engineering. Its research and develop- ment methodologies build on the scientic and de- sign methodologies in the classical areas, often with new elements to it. We take an in-depth look at one of the less well-understood methodologies in infor- matics, namely experimentation. What does it mean to do experiments in in- formatics? Does it make sense to `import' tradi- tional principles of experimentation from classical disciplines into the eld of computing and informa- tion processing? How should experiments be docu- mented? These are some of the questions that are treated. The report argues for the key role of empiri- cal research and experimentation in contemporary Informatics. Many IT systems, large and small, can only be designed sensibly with the help of experiments. We recommend that professionals and students alike are well-educated in the prin- ciples of sound experimentation in Informatics. We also recommend that experimentation protocols are used and standardized as part of the experimental method in Informatic

Experimental evaluation in computer science: a quantitative study

A survey of over 400 recent research articles suggests that computer scientists publish relatively few papers with experimentally validated results. The survey includes complete volumes of several refereed computer science journals, a conference, and 50 titles drawn at random from all articles published by ACM in 1993. The journals Optical Engineering (OE) and Neural Computation (NC) were used for comparison. Of the papers in the random sample that would require experimental validation, 40% have none at all. In journals related to software engineering, this fraction is over 50%.In comparison, the fraction of papers lacking quantitative evaluation in OE and NC is only 15% and 12%, respectively. Conversely, the fraction of papers that devote one fifth or more of their space to experimental validation is almost 70% for OE and NC,while it is a mere 30% for the CS random sample and 20% for software engineering.The low ratio of validated results appears to be a serious weakness in computer science research.This weakness should be rectified for the long-term health of the field

Informatics in the Future: Proceedings of the 11th European Computer Science Summit (ECSS 2015), Vienna, October 2015

Big data; Computing ethics; Women in computing; Research ethic