Natural language is the main presentation means in industrial requirements documents. This leads to the fact that requirements documents are often incomplete and inconsistent. Despite the fact that documents are mostly written in natural language, natural language processing (NLP) is barely used in industrial requirements engineering. The presented paper shows, how a natural language processing (NLP) approach can be integrated in a CASE tool. This enables the integrated tool to learn on the fly, which grammatical construction represents which model element. A valuable side effect of the proposed integration is tracing between the textual document and the constructed model. The presented integrated tool was evaluated in several case studies that have shown practical applicability of the tool.

Natural language is the main presentation means in industrial requirements documents. In such documents, system behavior is specified in the form of scenarios, with every scenario written as a sequence of sentences in natural language. To translate scenarios to executable models, message sequence charts (MSCs), we proposed an approach that analyzes textual scenarios by means of computational linguistics by L. Kof (2007). The presented paper shows that (1) a more differentiated treatment of certain sentence types than in our previous work results in better precision of the text-to-MSC translation and (2) it is possible to automate agent identification, performed semiautomatically in our previous work.

Abstract Formal verification of software systems is a challenge that is particularly important in the area of safety-critical automotive systems. Here, approaches like direct code verification are far too complicated, unless the verification is restricted to small textbook examples. Furthermore, the verification of application logic is of limited use in industrial context, unless the underlying operating system and the hardware are verified, too. This paper introduces a generic model stack, allowing the verification of all system layers as well as the concrete application models being used in the upper layers. The presented models and proofs close the gap between the correctness proof for the lower layers of car electronics developed at the Saarland University and the verification procedure for distributed applications developed at the Technische Universität München.

In industrial requirements documents natural language is the main presentation means. In such documents, system behavior is specified in the form of scenarios, written as a sequence of sentences in natural language. The scenarios are often incomplete: For the authors of requirements documents some facts are so obvious that they forget to mention them. This surely causes problems for the requirements analyst. This paper presents an approach that analyzes textual scenarios with the means of computational linguistics, identifies where communicating objects or whole actions are missing in the text, completes the missing information, and creates a message sequence chart (MSC) including the information missing in the textual scenario. Finally, this MSC is presented to the requirements analyst for validation. The paper presents also a case study in which scenarios from a requirement document based on industrial specifications were translated to MSCs. The case study shows the feasibility of the approach.

In today's software industry a software engineer is not only expected to successfully cope with technical challenges, but also to deal with non-technical issues arising from difficult project situations. These issues typically include understanding the customer's domain and requirements, working in a team, organizing the division of work, and coping with time pressure and hard deadlines. Thus, in our opinion teaching Software Engineering, (SE) not only requires studying theory using text books, but also providing students with the experience of typical non-technical issues in a software project. This article reports experiences with the concept of a course focusing on providing practical know-how.