Thi Ngoc Trang Tran

Sebastian Lubos, Alexander Felfernig, Damian Garber et al.

Configuration is a key technology for tailoring complex software systems, services, and products. A successful application of configurators not only depends on technical correctness, performance, and domain modeling but also on their usability. While general usability heuristics are widely used, configurator-specific criteria and tool support for systematic user interface (UI) analysis are limited. This paper explores the use of multimodal large language models (MLLMs) for scalable and semi-automated usability analysis of configurator UIs. We synthesize 18 configurator-specific usability criteria from the literature and apply these criteria in an MLLM-based analysis of 16 real-world configurators. Each criterion is assessed individually to generate severity ratings for usability issues and actionable improvement suggestions. A review of the results confirms that MLLMs can reliably identify configurator-specific usability issues and provide domain-aware improvement recommendations. Although human validation remains necessary, this approach has the potential to significantly reduce the required effort to analyze configurator usability.

Alexander Felfernig, Manfred Wundara, Thi Ngoc Trang Tran et al.

Sustainability development goals (SDGs) are regarded as a universal call to action with the overall objectives of planet protection, ending of poverty, and ensuring peace and prosperity for all people. In order to achieve these objectives, different AI technologies play a major role. Specifically, recommender systems can provide support for organizations and individuals to achieve the defined goals. Recommender systems integrate AI technologies such as machine learning, explainable AI (XAI), case-based reasoning, and constraint solving in order to find and explain user-relevant alternatives from a potentially large set of options. In this article, we summarize the state of the art in applying recommender systems to support the achievement of sustainability development goals. In this context, we discuss open issues for future research.

Viet-Man Le, Thi Ngoc Trang Tran, Sebastian Lubos et al.

We study whether Large Language Models (LLMs) can perform feature model analysis operations (AOs) directly on semi-formal textual blueprints, i.e., concise constrained-language descriptions of feature hierarchies and constraints, enabling early validation in Software Product Line scoping. Using 12 state-of-the-art LLMs and 16 standard AOs, we compare their outputs against the solver-based oracle FLAMA. Results show that reasoning-optimized models (e.g., Grok 4 Fast Reasoning, Gemini 2.5 Pro) achieve 88-89% average accuracy across all evaluated blueprints and operations, approaching solver correctness. We identify systematic errors in structural parsing and constraint reasoning, and highlight accuracy-cost trade-offs that inform model selection. These findings position LLMs as lightweight assistants for early variability validation.

Alexander Felfernig, Manfred Wundara, Thi Ngoc Trang Tran et al.

Sports recommender systems receive an increasing attention due to their potential of fostering healthy living, improving personal well-being, and increasing performances in sport. These systems support people in sports, for example, by the recommendation of healthy and performance boosting food items, the recommendation of training practices, talent and team recommendation, and the recommendation of specific tactics in competitions. With applications in the virtual world, for example, the recommendation of maps or opponents in e-sports, these systems already transcend conventional sports scenarios where physical presence is needed. On the basis of different working examples, we present an overview of sports recommender systems applications and techniques. Overall, we analyze the related state-of-the-art and discuss open research issues.

Sebastian Lubos, Alexander Felfernig, Thi Ngoc Trang Tran et al.

In contrast to single-user recommender systems, group recommender systems are designed to generate and explain recommendations for groups. This group-oriented setting introduces additional complexities, as several factors - absent in individual contexts - must be addressed. These include understanding group dynamics (e.g., social dependencies within the group), defining effective decision-making processes, ensuring that recommendations are suitable for all group members, and providing group-level explanations as well as explanations for individual users. In this paper, we analyze in which way large language models (LLMs) can support these aspects and help to increase the overall decision support quality and applicability of group recommender systems.

Viet-Man Le, Cristian Vidal Silva, Alexander Felfernig et al.

Constraint-based applications attempt to identify a solution that meets all defined user requirements. If the requirements are inconsistent with the underlying constraint set, algorithms that compute diagnoses for inconsistent constraints should be implemented to help users resolve the "no solution could be found" dilemma. FastDiag is a typical direct diagnosis algorithm that supports diagnosis calculation without predetermining conflicts. However, this approach faces runtime performance issues, especially when analyzing complex and large-scale knowledge bases. In this paper, we propose a novel algorithm, so-called FastDiagP, which is based on the idea of speculative programming. This algorithm extends FastDiag by integrating a parallelization mechanism that anticipates and pre-calculates consistency checks requested by FastDiag. This mechanism helps to provide consistency checks with fast answers and boosts the algorithm's runtime performance. The performance improvements of our proposed algorithm have been shown through empirical results using the Linux-2.6.3.33 configuration knowledge base.

Alexander Felfernig, Andrei Popescu, Mathias Uta et al.

Configuration is a successful application area of Artificial Intelligence. In the majority of the cases, configuration systems focus on configuring one solution (configuration) that satisfies the preferences of a single user or a group of users. In this paper, we introduce a new configuration approach - multi-configuration - that focuses on scenarios where the outcome of a configuration process is a set of configurations. Example applications thereof are the configuration of personalized exams for individual students, the configuration of project teams, reviewer-to-paper assignment, and hotel room assignments including individualized city trips for tourist groups. For multi-configuration scenarios, we exemplify a constraint satisfaction problem representation in the context of configuring exams. The paper is concluded with a discussion of open issues for future work.

Alexander Felfernig, Viet-Man Le, Andrei Popescu et al.

Recommender systems support decisions in various domains ranging from simple items such as books and movies to more complex items such as financial services, telecommunication equipment, and software systems. In this context, recommendations are determined, for example, on the basis of analyzing the preferences of similar users. In contrast to simple items which can be enumerated in an item catalog, complex items have to be represented on the basis of variability models (e.g., feature models) since a complete enumeration of all possible configurations is infeasible and would trigger significant performance issues. In this paper, we give an overview of a potential new line of research which is related to the application of recommender systems and machine learning techniques in feature modeling and configuration. In this context, we give examples of the application of recommender systems and machine learning and discuss future research issues.

Viet-Man Le, Alexander Felfernig, Mathias Uta et al.

Variability models (e.g., feature models) are a common way for the representation of variabilities and commonalities of software artifacts. Such models can be translated to a logical representation and thus allow different operations for quality assurance and other types of model property analysis. Specifically, complex and often large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we introduce DirectDebug which is a direct diagnosis approach to the automated testing and debugging of variability models. The algorithm helps software engineers by supporting an automated identification of faulty constraints responsible for an unintended behavior of a variability model. This approach can significantly decrease development and maintenance efforts for such models.