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.

This paper shows and critically discusses how experience gained from years of software engineering training in industry can be transferred to mass university education. The approach relies on cyclical, iterative and problem-based learning and places equal emphasis on technical skills (such as object-oriented and database programming) and soft skills (such as presentation techniques, handling personal conflicts and cooperating in a team context)

Financial services companies which heavily depend on IT, still face a shortage of well-educated software engineers. Of the two principal ways to react to this, either by training employees who are already IT-professionals or by introducing talented and interested nonprogrammers to software engineering, the latter was chosen by a large insurance company in Hamburg, Germany. This experience report points out why and how this approach, realized as a trainee program with a well-balanced mix of both technical and soft skills, could be as successful as it eventually turned out to be.

"Traditional" prototyping is based on assumptions that are not - or only partially - valid for the development of Web-based applications. In this paper, the "traditional" approaches of prototyping are recalled, and the new conditions, difficulties and challenges of involving and communicating with the relevant actors as a way of gathering sound requirements in the context of Web-projects are examined. The paper presents a modified prototyping approach called e-prototyping. It includes frequent releases of software versions (based on short development cycles) as well as gathering feedback from users and other relevant actors in "productive mode". The approach points at the need for offering various communication channels and systematically sorting out the stream of feedback to create sound requirements for evolutionary software process and release management.