This paper argues that understanding how professional software developers use diagrams and sketches in their work is an underexplored terrain. We illustrate this by summarizing a number of studies on sketching and diagramming across a variety of domains, and arguing for their limited generalizability. In order to develop further insight, we describe the design of a research project we are embarking upon and its grounding theoretical assumptions.

This paper reports on two simple innovations that helped create a more authentic and engaging modeling experience in an undergraduate Analysis and Design course: (a) having each team of students act both as modelers for another team, and as customers for another team, and (b) providing each team with their own whiteboard. The results from their use throughout the course, and for a single use of the whiteboards in a Computing Technology and Public Policy course, were quite positive. They resulted in a qualitatively different experience noticeable both to the instructors and the students. While some students were initially reluctant to use the whiteboards, by the end of the course most students were enthusiastic about their use.

Automated tests can play a key role in ensuring system quality in software development. However, significant problems arise in automating tests of stochastic algorithms. Normally, developers write tests that simply check whether the actual result is equal to the expected result (perhaps within some tolerance). But for stochastic algorithms, restricting ourselves in this way severely limits the kinds of tests we can write: either to trivial tests, or to fragile and hard-tounderstand tests that rely on a particular seed for a random number generator. A richer and more powerful set of tests is possible if we accommodate tests of statistical properties of the results of running an algorithm many times. The work described in this paper has been done in the context of a real-world application, a large-scale simulation of urban development designed to inform major decisions about land use and transportation. We describe our earlier experience with using automated testing for this system, in which we took a conventional approach, and the resulting difficulties. We then present a statistically based approach for testing stochastic algorithms based on hypothesis testing. Three different ways of constructing such tests are given, which cover the most commonly used distributions. We evaluate these tests in terms of frequency of failing when they should and when they should not, and conclude with guidelines and practical suggestions for implementing such unit tests for other stochastic applications.