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This paper describes and evaluates approaches to model and solve technical product configuration problems using different artificial intelligence methodologies. By means of a typical example, the benefits and limitations of different artificial intelligence methods are discussed and a flexible software architecture for integrating different solvers in a product configurator is proposed.

Background: Requirement engineering is often considered a critical activity in system development projects. The increasing complexity of software as well as number and heterogeneity of stakeholders motivate the development of methods and tools for improving largescale requirement engineering. Aims: The empirical study presented in this paper aim to identify and understand the characteristics and challenges of a platform, as desired by experts, to support requirement engineering for individual stakeholders, based on the current pain-points of their organizations when dealing with a large number requirements. Method: We conducted a multiple case study with three companies in different domains. We collected data through ten semi-structured interviews with experts from these companies. Results: The main pain-point for stakeholders is handling the vast amount of data from different sources. The foreseen platform should leverage such data to manage changes in requirements according to customers' and users' preferences. It should also offer stakeholders an estimation of how long a requirements engineering task will take to complete, along with an easier requirements dependency identification and requirements reuse strategy. Conclusions: The findings provide empirical evidence about how practitioners wish to improve their requirement engineering processes and tools. The insights are a starting point for in-depth investigations into the problems and solutions presented. Practitioners can use the results to improve existing or design new practices and tools.Requirements are often considered the basis for all subsequent development, deployment, and maintenance activities.Poorly implemented Requirements Engineering (RE) presents significant risks for a project [3], including its cancellation or additional costs [13]. Gartner research found that requirements are the third source of product defects and the first source of delivered defects for service projects. Accordingly, the cost of fixing defects ranges from $70 at the requirements phase to $14.000 in production phase 1 .Nevertheless, RE often receives little project effort [9] and, in spite of the advances in the field, practitioners still struggle with it [10,19]. For instance, a recent study [7] reports that the requirements definition is a challenge for practitioners.At the same time, the latest advancements in machine learning and natural language processing bear new potentials to support decision-makers in the context of RE [15,17]. As organizations are constantly looking for ways to produce novel products and services, a platform-embedding the above technologies to support the RE process-can increase stakeholders' and customers' satisfaction, improve time to market, and reduce costs.The research project OpenReq 2 aims to develop such a platform for companies dealing with large-scale requirementse.g., in the order of thousands per project [25]. In particular, OpenReq covers industial scenarios related to bid management for railways, community-driven cross-platform de...

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