An Experimental Study of Design Information Reuse

Proceedings of the 4th International Conference on Design Theory and Methodology. 1992
An Experimental Study of Design Information Reuse
Vinod Baya, Jody Gevins, Catherine Baudin, Ade Magogunje, Larry Leifer

We are reporting results and experiences from an experimental study conducted to study the nature of design information reuse during redesign. Starting with a detailed study of the questioning behavior of two designers, we have developed a framework for understanding the character and the basic constitution of information that should be recorded during design for the reuse process to be useful and productive.

This study lays the ground work for future work in recording, characterizing and indexing design information as it is generated during the design process.

1 Introduction Design in any engineering domain is a very complex activity. We as researchers look at this activity from various perspectives such as technical, methodological, social 1 Jody Gevins is a contractor at Sterling software systems and Catherine Baudin at RECOM inc.

Another publication from the same category: Machine Learning and Data Science

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.