The Case for XCIP: Bridging User and Data Provider Expectations in Research Infrastructures

Data integration services are of utmost importance in research data infrastructures like the NFDI, enabling the harmonization of diverse data sources. While significant progress has been made in automatic schema mapping and protocol development, non-functional requirements play a vital role in ensuring fair and transparent collaboration between data infrastructures and providers. Key non-functional requirements include access control, resource usage limitations, quota tracking, and usage statistics reporting. These are crucial for maintaining provider trust and data integrity. For instance, access control ensures that only authorized users can access sensitive data, while usage statistics allow providers to track the impact of their contributions. Balancing these requirements with user experience presents a challenge. End-users expect scalable and fast data access without noticeable restrictions, necessitating a solution that addresses both provider needs and user expectations. To address this challenge, we present the XCIP (eXchange Component for Infrastructure Providers), a novel exchange component designed to facilitate connection and communication between data infrastructures and providers, which builds upon our previous work, the SDExL (Spatiotemporal Data Exchange Layer). XCIP not only addresses non-functional requirements but also introduces innovative mechanisms for data caching and access optimization. These features enhance data accessibility for end-users while maintaining transparency, effectively bridging the gap between provider requirements and user expectations. This paper details the design of XCIP, demonstrating how it addresses non-functional requirements while simultaneously improving the end-user experience through transparent data caching and statistics collection

Towards (More) Effective Visualizations for Process Discovery

Visualizations in process mining are essential for communicating results to users and for enabling informed decision-making. However, so far, the existing visualizations are often not tailored to the specific process mining tasks, difficult to interpret, and struggle to show the process complexity of the underlying event logs. Furthermore, the user’s perception of the visualizations is rarely analyzed. Researchers have acknowledged the need for suitable visualizations and the consideration of the user’s cognitive perspective to improve the organizational insights gained from them. This dissertation aims to address this need by (1) empirically evaluating existing visualizations to identify potentials and limitations, (2) developing new visualizations, and (3) empirically testing the effectiveness of these (new) visualizations to support process discovery tasks

Factors involved in Modernization Decision Making

Many software systems in use today were developed decades ago, becoming outdated and difficult to maintain.Since these systems are often the backbone of businesses, modernization is crucial to remain competitive. Despite the importance of modernization, research on decision-making in this area is limited. Existing studies often overlook the interdependencies between factors. To address this, a Systematic Literature Review was conducted to identify key factors, their relationships, and stakeholder perspectives. The goal was to develop a conceptual model to support informed organizational decisions

C to Rust Translation via Large Language Models

Generative AI shows great potential for various software engineering tasks. In this paper, we focus on translation of embedded C code to Rust using Large Language Models (LLMs). We detail on specific issues and show concrete remedies in task formulation with code. These remedies are general and can be leveraged across various software engineering use cases

Towards an EA-based approach for the development of Digital Twins for Sustainable Building Renovation Decision-Making by Real Estate Trustees

In the Architecture, Engineering, Construction & Operations (AECO) sector, Digital Twins (DTs) serve as transformative tools to integrate sustainability into renovation decision-making by providing dynamic, data-driven representations of physical assets. They enable stakeholders to optimise processes, improve resource efficiency, and ensure compliance with evolving regulations. However, the implementation of DTs in this context remains challenged by fragmented data ecosystems, insufficient regulatory integration, and limited consideration of sustainability goals. Enterprise Architecture (EA) offers a structured approach to overcoming these challenges by aligning technical, organisational, and sustainability-related objectives. To advance this, we define two key research objectives: (1) assessing current EA approaches for integrating DTs, and (2) developing sustainability-oriented EA modelling concepts and design patterns. Our aim is to position DTs as comprehensive decision-support tools for sustainability, enabling stakeholders to achieve better-informed decisions while addressing regulatory compliance, resource efficiency, and life cycle impacts

Requirements und KI jenseits vom Prompt Engineering: Diskussion beim GI FG RE Treffen 2024

Das Jahrestreffen 2024 der Fachgruppe „Requirements-Engineering“ (FG RE) in der Gesellschaft für Informatik (GI e.V.) stand unter dem Leitthema „GenAI und RE“. In einer abschließenden Plenumsdiskussion tauschten sich die Teilnehmenden über ihre Eindrücke aus und trugen wichtige Punkte zusammen. Dieser Bericht beschreibt den Verlauf der Diskussion und dokumentiert die Ergebnisse

Self-Refinement Strategies for LLM-based Product Attribute Value Extraction

Structured product data, represented as attribute-value pairs, is crucial for e-commerce platforms to enable features such as faceted product search and attribute-based product comparison. However, vendors often supply unstructured product descriptions, necessitating attribute value extraction to ensure data consistency and usability. Large language models (LLMs), including OpenAI's GPT-4o, have demonstrated their potential for product attribute value extraction in few-shot scenarios. Recent research has shown that self-refinement techniques can improve the performance of LLMs on tasks such as code generation and text-to-SQL translation. For other tasks, applying these techniques has only led to increased costs due to the processing of additional tokens, without achieving an improved performance. This paper investigates applying two self-refinement techniques — error-based prompt rewriting and self-correction — to the product attribute value extraction task. The self-refinement techniques are evaluated across zero-shot, few-shot in-context learning, and fine-tuning scenarios. Experimental results reveal that both self-refinement techniques have a marginal impact on the performance of GPT-4o across the different scenarios while significantly increasing processing costs. For attribute value extraction scenarios involving training data, fine-tuning yields the highest performance while the ramp-up costs of fine-tuning are balanced out as the amount of product descriptions grows

Workshop Summary

The main goal of the workshop GenSE’25 is to discuss the newest developments in the area of generative artificial intelligence in the context of software engineering and their practical applications. In order to successfully apply generative AI methods in software engineering, it is particularly important to analyze and critically reflect on issues about the trustworthiness and robustness of this technology. To overcome these issues, one of the main topics of the workshop was selected to be neurosymbolic methods, i.e. those methods that combine subsymbolic (machine learning) with symbolic approaches (e.g. knowledge representation and inference based on symbolic logic) to improve the reliability of generative AI methods

Real-world labs for digital transformation in forestry

Forests play a central role in our ecosystem and have a major influence on our climate, biodiversity, and quality of life. According to the Forest Condition Report, only one in five trees is currently considered healthy. Climate change and environmental stressors like heat and drought challenge forests globally, making protection and adaptation essential. Digital technologies offer new opportunities for sustainable forest management, with real-world labs (RWLs) playing a key role. RWLs allow to test innovative solutions in real environments and foster collaboration between scientists, policymakers, businesses and the public. In the form of a case study, we employ an RWL approach for digitization in forestry, which uses IoT sensors to monitor forests and collect data. Transdisciplinary workshops bring together diverse stakeholders to discuss these technologies and co-create solutions. This paper summarizes the RWL experiences in this context, summarizing results and experiences