5 citations · 15 across the 10 of their papers we have counts for
12 papers
Transforming Privacy Artifacts into Accessible Reports for Non-Technical Stakeholders
Zoe Pfister, Clemens Sauerwein, Benedikt Dornauer +4
The transition toward Industry 5.0 is reshaping industrial work environments with an emphasis on human-centricity, enabling close collaboration between humans and machines to enhan…
RESTestBench: A Benchmark for Evaluating the Effectiveness of LLM-Generated REST API Test Cases from NL Requirements
Leon Kogler, Stefan Hangler, Maximilian Ehrhart +3
Existing REST API testing tools are typically evaluated using code coverage and crash-based fault metrics. However, recent LLM-based approaches increasingly generate tests from NL…
RAG-DIVE: A Dynamic Approach for Multi-Turn Dialogue Evaluation in Retrieval-Augmented Generation
Lorenz Brehme, Benedikt Dornauer, Jan-Henrik Böttcher +3
Evaluating Retrieval-Augmented Generation (RAG) systems using static multi-turn datasets fails to capture the dynamic nature of real-world dialogues. Existing evaluation methods re…
RAGnaroX: A Secure, Local-Hosted ChatOps Assistant Using Small Language Models
Benedikt Dornauer, Mircea-Cristian Racasan
This paper introduces RAGnaroX, a resource-efficient ChatOps assistant that operates entirely on commodity hardware. Unlike existing solutions that often rely on external providers…
RESTifAI: LLM-Based Workflow for Reusable REST API Testing
Leon Kogler, Maximilian Ehrhart, Benedikt Dornauer +1
With this paper, we introduce RESTifAI, an LLM-driven approach for generating reusable, CI/CD ready REST API tests, following the happy-path approach. Unlike existing tools that of…
The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Project
Robin Gröpler, Steffen Klepke, Jack Johns +12
Generative AI (GenAI) has recently emerged as a groundbreaking force in Software Engineering, capable of generating code, identifying bugs, recommending fixes, and supporting quali…
Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation
Lorenz Brehme, Benedikt Dornauer, Thomas Ströhle +2
Retrieval-Augmented Generation (RAG) is a well-established and rapidly evolving field within AI that enhances the outputs of large language models by integrating relevant informati…
How Industry Tackles Anomalies during Runtime: Approaches and Key Monitoring Parameters
Monika Steidl, Benedikt Dornauer, Michael Felderer +3
Deviations from expected behavior during runtime, known as anomalies, have become more common due to the systems' complexity, especially for microservices. Consequently, analyzing…
On the Role of Font Formats in Building Efficient Web Applications
Benedikt Dornauer, Wolfgang Vigl, Michael Felderer
The success of a web application is closely linked to its performance, which positively impacts user satisfaction and contributes to energy-saving efforts. Among the various optimi…
Web Image Formats: Assessment of Their Real-World-Usage and Performance across Popular Web Browsers
Benedikt Dornauer, Michael Felderer
In 2023, images on the web make up 41% of transmitted data, significantly impacting the performance of web apps. Fortunately, image formats like WEBP and AVIF could offer advanced…
SoHist: A Tool for Managing Technical Debt through Retro Perspective Code Analysis
Benedikt Dornauer, Michael Felderer, Johannes Weinzerl +2
Technical debt is often the result of Short Run decisions made during code development, which can lead to long-term maintenance costs and risks. Hence, evaluating the progression o…
Energy-Saving Strategies for Mobile Web Apps and their Measurement: Results from a Decade of Research
Benedikt Dornauer, Michael Felderer
In 2022, over half of the web traffic was accessed through mobile devices. By reducing the energy consumption of mobile web apps, we can not only extend the battery life of our dev…