Design by Contract, Components and Debugging

title: The Component Combinator for Enterprise Applications

title: Library Design

title: Providing Trusted Components to the Industry
abstract: The software industry stands on feet of clay. However carefully we may strive to build correct and reliable software, we have no way of guaranteeing the quality of the result. Building correct and reliable software depends on the quality of so much else, from the hardware and the operating system to the compiler and the runtime libraries. And any significant software system has so many details and components of its own that we can hardly expect to get everything right if we do it all by ourselves. We describe an ambitious but realistic project that combines the ideas of reuse and formality with other more pragmatic techniques. The goal is to provide the entire software industry with a powerful set of reusable components deserving a high degree of trust.

title: Dependable Software
abstract: Achieving software reliability takes many complementary tech-niques, directed at the process or at the products. This survey summarizes some of the most fruitful ideas.

title: Concurrent object-oriented programming on .NET
abstract: The SCOOP model (Simple concurrent object-oriented programming) offers a comprehensive approach to building high-quality concurrent and distributed systems. The model takes advantage of the inherent concurrency implicit in object-oriented programming to provide programmers with a simple extension enabling them to produce concurrent applications with little more effort than sequential ones. In the paper, the authors discuss the basic concepts of the model, such as processors and separate objects. They also present SCOOPLI, a library implementation of SCOOP for the .NET platform. They show how SCOOP concepts are mapped to .NET constructs, and discuss distributed programming with SCOOPLI, with a focus on .NET Remoting capabilities. Several programming examples illustrate the discussion.

title: Age group differences in world wide web navigation
abstract: In this study, we examined the effects of age and training on efficiency and preferences in a World Wide Web search activity. Older participants were able to complete most of the tasks, but took more steps to find the information than did younger adults. Factors in this inefficiency were patterns of returning to the home page and revisiting pages that had been seen before during a search. Interactive training improved efficiency and altered preferences. We discuss implications for training and design.

title: Issues in practical application of an adaptive interface
abstract: The authors have been developing a prototype sys[em for installation in an operationat business environment. During the development, a number of issues have been encountered. These include: ● constraints arising from placing prototypes in operational environments . lack of guidelines for selecting types of adaptation ● difficulty determining adaptation criteria ● difficulty obtaining necessary data from users ● lack of guidelines for making information display decisions ● problems in testing ‘real world’ interfaces These issues are not readily solved with more sophisticated adaptation algorithms; rather. they point out the need for CO1Iect ing more information from attempts to bring intelligent interfaces to complex business environments.

title: Prediction of Material Properties from Chemical Structures. The Clearing Temperature of Nematic Liquid Crystals Derived from Their Chemical Structures by Artificial Neural Networks
abstract: The prediction of properties of molecules just on the basis of their chemical structures is desirable to selectively make molecules that have the wanted properties, like biological activity, viscosity, or toxicity. Here, we present an example of a new way to predict a property from the chemical structure of a chemically heterogeneous class of compounds. The clearing temperatures of nematic liquid-crystalline phases of 17383 compounds were used to train neural networks to derive this material property directly from their chemical structure. The trained neural networks were subsequently tested with 4345 structural patterns of molecules unknown to the networks to assess their predictive value. The clearing temperatures were predicted by the best network with a standard deviation of 13 degrees.