: The development of knowledge-based systems requires the availability of domain and problem-solving knowledge. This knowledge must be acquired from domain experts. For this purpose the paper proposes to adopt the concept of scenarios known from requirements engineering and object oriented modelling to enrich the set of acquisition techniques. In RE and OO scenarios are used to define a vision of a future system. In knowledge engineering their scope has to be extended, but their advantages (ease of understanding, support of communication, focus on relevant aspects etc.) still hold for knowledge-based systems. 1 Motivation In the knowledge acquisition (KA) community it is common sense that for developing knowledgebased systems (KBS), a modelling approach has to be pursued to guarantee reliable, traceable, and maintainable systems of high quality. A prerequisite for such systems is the availability of the needed knowledge. Most current KA methods support knowledge engineers in ...

The study of contemporary Requirements Engineering (RE) methodologies indicates that modeling of organizational goals constitutes a central activity of the RE process. In particular, goals provide the rationale and drive the elaboration of the requirements that operationalise them. They also provide the criteria against which the completeness and correctness of the requirements specification is validated. In other words, requirements implement goals in the same way that programs implement design specifications. Despite the significance of goals in RE, research in the field is fragmented. No research has so far taken place in order to define the overall role that goals play in RE. This paper puts forward a unifying view of goal analysis in the context of RE. This allows the identification of similarities and differences between the different conceptions of goal used by different approaches and promotes the understanding of the overall role of goal analysis in RE. Based on this understanding the various approaches can be put together thus leading to a stronger goal-driven RE framework that takes advantage of the contributions from the many streams of goal-oriented research. Keywords: Goal-oriented requirements engineering, method integration, goal analysis, goal modelling.

We reconsider validation and maintenance characteristics of software systems under the analogy between software science and philosophy of science or, more precisely, between software construction and machine learning (ML). From this outset, many classical techniques from ML can be used. In particular, we adapt a constructive extension of reinforcement learning to address the question in a formal way. We define a measure of software 'predictiveness', which is identified with soft- ware validation, to represent the stability of a system. An inversely related measure, the probability of modification, is also obtained for each component and for the whole system. The application in practice of these measurements is discussed. From here, we present some models of maintenance cost based on a detailed combination of predictiveness and modifiabiHty. We study different software arrangement topologies theoretically. Hierarchised topologies, especially downward confluent ones such as trees and lattices involve less maintenance costs. Moreover, some intuitive rationales are confirmed, namely that compressed systems and coherent models (without patches or exceptions) are manifestly more maintainable.

The continuous use of the Web for daily operations by businesses, consumers, and the government has created a great demand for reliable Web applications. One promising approach to testing the functionality of Web applications leverages the usersession data collected by Web servers. User-session-based testing automatically generates test cases based on real user profiles. The key contribution of this paper is the application of concept analysis for clustering user sessions and a set of heuristics for test case selection. Existing incremental concept analysis algorithms are exploited to avoid collecting and maintaining large user-session data sets and to thus provide scalability. We have completely automated the process from user session collection and test suite reduction through test case replay. Our incremental test suite update algorithm, coupled with our experimental study, indicates that concept analysis provides a promising means for incrementally updating reduced test suites in response to newly captured user sessions with little loss in fault detection capability and program coverage.