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The number of embedded systems in our daily lives that are distributed, hidden, and ubiquitous continues to increase. Many of them are safety-critical. To provide additional or better functionalities, they are becoming more and more complex, which makes it difficult to guarantee safety. It is undisputed that safety must be considered before the start of development, continue until decommissioning, and is particularly important during the design of the system and software architecture. An architecture must be able to avoid, detect, or mitigate all dangerous failures to a sufficient degree. For this purpose, the architectural design must be guided and verified by safety analyses. However, state-of-the-art component-oriented or model-based architectural design approaches use different levels of abstraction to handle complexity. So, safety analyses must also be applied on different levels of abstraction, and it must be checked and guaranteed that they are consistent with each other, which is not supported by standard safety analyses. In this paper, we present a consistency check for CFTs that automatically detects commonalities and inconsistencies between fault trees of different levels of abstraction. This facilitates the application of safety analyses in top-down architectural designs and reduces effort.
ESEM-Untersuchungen zum Verfilmungsverhalten von Latex-Dispersionen in Wasser und Zementporenlösung
(2010)
Online Identification of Learner Problem Solving Strategies Using Pattern Recognition Methods
(2010)
Learning and programming environments used in computer science education give feedback to the users by system messages. These are triggered by programming errors and give only "technical" hints without regard to the learners' problem solving process. To adapt the messages not only to the factual but also to the procedural knowledge of the learners, their problem solving strategies have to be identified automatically and in process. This article describes a way to achieve this with the help of pattern recognition methods. Using data from a study with 65 learners aged 12 to 13 using a learning environment for programming, a classification system based on hidden Markov models is trained and integrated in the very same environment. We discuss findings in that data and the performance of the automatic online identification, and present first results using the developed software in class.