Many development projects of complex, interdisciplinary products fail due to an inefficient handling of requirement changes. One reason for this is that requirement dependencies and the resulting change propagation are not sufficiently recognized and considered by existing approaches. Based on a literature study and two industry workshops, approaches of automated dependency analysis of requirements for interdisciplinary development are evaluated. The result is that ontology-based approaches are particularly suitable for the automated dependency analysis of requirements due to the use of expert knowledge and the possibility of automation. On this basis, subsequent research activities can further develop the handling of requirements changes in interdisciplinary projects in a targeted manner.

Innovation projects are characterized by numerous uncertainties. Typical concepts in development management like the application of safety coefficients imply limitations of the solution space. In contrast, explicit handling of uncertainties can support engineers in understanding the problem space and in utilising the full potential of the design space along iterative product development steps. As a result from literature analysis, there is a lack of a support for product development that addresses the specific problem of uncertainty and risk in the context of requirement changes. The aim of the contribution at hand is to enhance the efficient development of complex interdisciplinary systems by enabling uncertainty handling in requirements change management. Based on a classification of uncertainty types resulting in a descriptive model, risk management measures are identified to support requirements engineers. The proposed method includes identification & modelling, analysis, treatment and monitoring of risks and counter-measures. By applying this method, engineers are supported in adopting agile approaches and enabling flexible Requirements Engineering.

Requirements changes are a leading cause for project failures. Due to propagation effects, change management requires dependency analysis. Existing approaches have shortcomings regarding ability to process large requirement sets, availability of required data, differentiation of propagation behavior and consideration of higher order dependencies. This paper introduces a new method for advanced requirement dependency analysis based on machine learning. Evaluation proves applicability and high performance by means of a case example, 4 development projects and 3 workshops with industry experts.

Effect chain modeling approaches are applied to model cause-effect relations and analyze affected elements and dependencies. In this paper a systematic literature research is conducted to derive main characteristics and limitations of existing approaches. Then, the Model-based Effect Chain Analysis (MECA) method is introduced. Evaluation proves applicability of the method by means of a case example. This is done in the context of a project with a German automotive company. In the project 66 workshops were conducted to model certification-compliant effect chains in accordance to the UN ECE 156.