The ability to efficiently resolve conflicts in interactive constraint-based applications is critical for user experience and system reliability. Conflict resolution can be regarded as a specific type of explanation, often denoted as diagnosis. Existing work on integrating machine learning with diagnostic reasoning emphasizes on the combination of hitting set approaches with probabilistic reasoning and memory-based machine learning. An alternative to such two-phase diagnosis approaches is direct diagnosis, which focuses on determining diagnoses without predetermining conflicts. In this article, we utilize diagnosis knowledge from the past to improve diagnosis efficiency while also maintaining user-defined preference criteria. Our approach integrates model-based collaborative filtering (feed-forward neural networks) and other machine learning approaches (e.g., logistic regression and random forest) with direct model-based diagnosis (FastDiag). The re-ordering of constraints as input to the diagnosis algorithm increases the efficiency of diagnostic reasoning for determining preference-preserving diagnoses. Through experiments on real-world configuration knowledge bases (B2C, BusyBox, EA and Linux kernel), we demonstrate significant runtime improvements and high accuracy in diagnosis prediction. With this, we also contribute to the growing body of literature on combining machine learning and constraint-based reasoning.

Successful software projects depend on the quality of software requirements. Creating high-quality requirements is a crucial step toward successful software development. Effective support in this area can significantly reduce development costs and enhance the software quality. In this paper, we introduce and assess the capabilities of a Large Language Model (LLM) to evaluate the quality characteristics of software requirements according to the ISO 29148 standard. We aim to further improve the support of stakeholders engaged in requirements engineering (RE). We show how an LLM can assess requirements, explain its decision-making process, and examine its capacity to propose improved versions of requirements. We conduct a study with software engineers to validate our approach. Our findings emphasize the potential of LLMs for improving the quality of software requirements.

Sports recommender systems receive an increasing attention due to their potential of fostering healthy living, improving personal well-being, and increasing performances in sports. 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 examples, we present an overview of sports recommender systems applications and techniques. Overall, we analyze the related state-of-the-art and discuss future research directions.

Recommender systems play an important role in supporting the achievement of the United Nations sustainable development goals (SDGs). In recommender systems, explanations can support different goals, such as increasing a user's trust in a recommendation, persuading a user to purchase specific items, or increasing the understanding of the reasons behind a recommendation. In this paper, we discuss the concept of "sustainability-aware persuasive explanations" which we regard as a major concept to support the achievement of the mentioned SDGs. Such explanations are orthogonal to most existing explanation approaches since they focus on a "less is more" principle, which per se is not included in existing e-commerce platforms. Based on a user study in three item domains, we analyze the potential impacts of sustainability-aware persuasive explanations. The study results are promising regarding user acceptance and the potential impacts of such explanations.

Recommender systems are decision support systems that help users to identify items of relevance from a potentially large set of alternatives. In contrast to the mainstream recommendation approaches of collaborative filtering and content-based filtering, knowledge-based recommenders exploit semantic user preference knowledge, item knowledge, and recommendation knowledge, to identify user-relevant items which is of specific relevance when dealing with complex and high-involvement items. Such recommenders are primarily applied in scenarios where users specify (and revise) their preferences, and related recommendations are determined on the basis of constraints or attribute-level similarity metrics. In this article, we provide an overview of the existing state-of-the-art in knowledge-based recommender systems. Different related recommendation techniques are explained on the basis of a working example from the domain of survey software services. On the basis of our analysis, we outline different directions for future research.

Conflict detection is relevant in various application scenarios, ranging from interactive decision-making to the diagnosis of faulty knowledge bases. Conflicts can be regarded as sets of constraints that cause an inconsistency. In many scenarios (e.g., constraint-based configuration), conflicts are repeatedly determined for the same or similar sets of constraints. This misses out on the valuable opportunity for leveraging knowledge reuse and related potential performance improvements, which are extremely important, specifically interactive constraint-based applications. In this paper, we show how to integrate knowledge reuse concepts into non-instructive conflict detection. We introduce the InformedQX algorithm, which is a reuse-aware variant of QuickXPlain. The results of a related performance analysis with the Linux-2.6.3.33 configuration knowledge base show significant improvements in terms of runtime performance compared to QuickXPlain.

Compilers are highly configurable systems. One can influence the performance of a compiled program by activating and deactivating selected compiler optimizations. However, automatically finding well-performing configurations is a challenging task. We consider expensive iteration, paired with recompilation of the program to optimize, as one of the main shortcomings of state-of-the-art approaches. Therefore, we propose Optimization Space Learning, a lightweight and noniterative technique. It exploits concepts known from configuration space learning and recommender systems to discover well-performing compiler configurations. This reduces the overhead induced by the approach significantly, compared to existing approaches. The process of finding a well-performing configuration is 800k times faster than with the state-of-the-art techniques.

Users are often confronted with situations where they have to decide in favor or against an offered item, like a book, movie, or recipe. Those suggested items are commonly determined by a recommender system, which considers personal preferences to identify relevant items. However, those systems often lack transparency and comprehensibility in revealing why a specific item is recommended. For this purpose, explanations have been added as a powerful tool to help users with their final decisions. In this paper, we present and evaluate the capabilities of a Large Language Model (LLM) to come up with high-quality explanations to further improve the support of users for three different recommendation approaches, including feature-based recommendation, collaborative filtering, and knowledge-based recommendation. We explain how an LLM can be applied to generate personalized explanations and evaluate the explanation goals in an online user study. Our findings highlight that LLM-generated explanations are highly appreciated by users as they help in the evaluation of recommended items. Furthermore, we discuss which characteristics of the LLM-based explanations were perceived positively and how those findings can be used for future research.

The increasing size and complexity of feature models (FMs) can trigger anomalies or faults, challenging stakeholders in keeping FMs consistent with the domain requirements. Existing quality assurance tools do not provide advanced techniques to point out possibilities to adapt an FM for consistency recovery. In this paper, we present FMTESTING, which is a plug-in for FEATUREIDE, an ECLIPSE-based IDE supporting different phases of feature-oriented software development. FMTESTING is capable of automatically generating property-based test cases based on six different types of FM analysis operations. Furthermore, for violated test cases, diagnoses are provided to precisely indicate faulty FM elements (constraints) that should be adapted to restore consistency. Our tool provides user interfaces inside FEATUREIDE to ensure convenient use, even for users who are not domain experts.

Feature model configuration can be supported on the basis of various types of reasoning approaches. Examples thereof are SAT solving, constraint solving, and answer set programming (ASP). Using these approaches requires technical expertise of how to define and solve the underlying configuration problem. In this paper, we show how to apply conjunctive queries typically supported by today's relational database systems to solve constraint satisfaction problems (CSP) and -- more specifically -- feature model configuration tasks. This approach allows the application of a wide-spread database technology to solve configuration tasks and also allows for new algorithmic approaches when it comes to the identification and resolution of inconsistencies.

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.

Group decision-making processes can be supported by group recommender systems that help groups of users obtain satisfying decision outcomes. These systems integrate a consensus-achieving process, allowing group members to discuss with each other on the potential items, adapt their opinions accordingly, and achieve an agreement on a selected item. Such a process, therefore, helps to generate group recommendations with a high satisfaction level of group members. Our article provides a rigorous review of the existing consensus approaches to group decision-making. These approaches are classified depending on the applied consensus models such as reference domain where a set of group members or items is selected for calculating consensus measures, coincidence method that calculates the consensus degree between group members depending on the coincidence concept, operators that aggregate user preferences, guidance measures where the consensus-achieving process is guided by different consensus measures, and recommendation generation and individual centrality that enhance the role of a moderator or a leader in the consensus-achieving process. Further consensus techniques for group decision-making in heterogeneous and large-scale groups are also discussed in this article. Besides, to provide an overall landscape of consensus approaches, we also discuss new consensus models in group recommender systems. These models attempt to improve basic aggregation strategies, further consider social relationship interactions, and provide group members with intuitive descriptions regarding the current consensus state of the group. Finally, we point out challenges and discuss open topics for future work.

The development and maintenance of feature models is often an error-prone activity requiring different types of analysis operations that help developers to restore required feature model properties. Fulfilling such properties helps to assure compliance between feature model and corresponding domain variability properties and -at the same time - helps to increase feature model maintainability. In this paper, we propose a set of additional analysis operations that provide insights regarding potential impacts of applying feature models in constraint-based recommendation scenarios where feature models are used to define user preference spaces. Our proposed analysis operations provide a.o. insights into aspects such as feature restrictiveness and product accessibility when applying a constraint-based recommender system. We analyze usage scenarios of the operations on the basis of an example implementation with a digital camera feature model and discuss open research issues.

The development and maintenance of feature models is often an error-prone activity requiring different types of analysis operations that help developers to restore required feature model properties. Fulfilling such properties helps to assure compliance between feature model and corresponding domain variability properties and -at the same time - helps to increase feature model maintainability. In this paper, we propose a set of additional analysis operations that provide insights regarding potential impacts of applying feature models in constraint-based recommendation scenarios where feature models are used to define user preference spaces. Our proposed analysis operations provide a.o. insights into aspects such as feature restrictiveness and product accessibility when applying a constraint-based recommender system. We analyze usage scenarios of the operations on the basis of an example implementation with a digital camera feature model and discuss open research issues.

In many scenarios, configurators support the configuration of a solution that satisfies the preferences of a single user. The concept of \emph{multi-configuration} is based on the idea of configuring a set of configurations. Such a functionality is relevant in scenarios such as the configuration of personalized exams, the configuration of project teams, and the configuration of different trips for individual members of a tourist group (e.g., when visiting a specific city). In this paper, we exemplify the application of multi-configuration for generating individualized exams. We also provide a constraint solver performance analysis which helps to gain some insights into corresponding performance issues.

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.