Revealing the Copy and Paste Habits of End Users

Transferring data across applications is a common end user task, and copying and pasting via the clipboard lets users do so relatively easily. Using the clipboard, however, can also introduce inefficiencies and errors in user tasks. To help researchers and tool developers understand and address these problems, we studied how end users interact with the clipboard through cut, copy, and paste actions. This study was performed by logging clipboard interactions while end users performed everyday tasks. From the clipboard usage data, we have identified several usage patterns that describe how data is transferred within the desktop environment. Such patterns help us understand end user behavior and indicate areas in which clipboard support tools can be improved.

Review of Applied Spatial Data Analysis with R by R. S. Bivand, E. J. Pebesma, and V. Gomez-Rubio

Most authors struggle to pick a title that adequately conveys all of the material covered in a book. When I first saw Applied Spatial Data Analysis with R, I expected a review of spatial statistical models and their applications in packages (libraries) from the CRAN site of R. The authors’ title is not misleading, but I was very pleasantly surprised by how deep the word “applied” is here. The first half of the book essentially covers how R handles spatial data. To some statisticians this may be boring. Do you want, or need, to know the difference between S3 and S4 classes, how spatial objects in R are organized, and how various methods work on the spatial objects? A few years ago I would have said “no,” especially to the “want” part. Just let me slap my EXCEL spreadsheet into R and run some spatial functions on it. Unfortunately, the world is not so simple, and ultimately we want to minimize effort to get all of our spatial analyses accomplished. The first half of this book certainly convinced me that some extra effort in organizing my data into certain spatial class structures makes the analysis easier and less subject to mistakes. I also admit that I found it very interesting and I learned a lot.

Helping End-Users “Engineer” Dependable Web Applications

End-user programmers are increasingly relying on web authoring environments to create web applications. Although often consisting primarily of web pages, such applications are increasingly going further, harnessing the content available on the web through “programs” that query other web applications for information to drive other tasks. Unfortunately, errors can be pervasive in web applications, impacting their dependability. This paper reports the results of an exploratory study of end-user web application developers, performed with the aim of exposing prevalent classes of errors. The results suggest that end-users struggle the most with the identification and manipulation of variables when structuring requests to obtain data from other web sites. To address this problem, we present a family of techniques that help end user programmers perform this task, reducing possible sources of error. The techniques focus on simplification and characterization of the data that end-users must analyze while developing their web applications. We report the results of an empirical study in which these techniques are applied to several popular web sites. Our results reveal several potential benefits for end-users who wish to “engineer” dependable web applications.