Daniel Seifert

Adam Trendowicz, Daniel Seifert, Andreas Jedlitschka et al.

Generative artificial intelligence (GAI), specifically large language models (LLMs), are increasingly used in software engineering, mainly for coding tasks. However, requirements engineering - particularly requirements validation - has seen limited application of GAI. The current focus of using GAI for requirements is on eliciting, transforming, and classifying requirements, not on quality assessment. We propose and evaluate the LLM-based (GPT-4o) approach "DeepQuali", for assessing and improving requirements quality in agile software development. We applied it to projects in two small companies, where we compared LLM-based quality assessments with expert judgments. Experts also participated in walkthroughs of the solution, provided feedback, and rated their acceptance of the approach. Experts largely agreed with the LLM's quality assessments, especially regarding overall ratings and explanations. However, they did not always agree with the other experts on detailed ratings, suggesting that expertise and experience may influence judgments. Experts recognized the usefulness of the approach but criticized the lack of integration into their workflow. LLMs show potential in supporting software engineers with the quality assessment and improvement of requirements. The explicit use of quality models and explanatory feedback increases acceptance.

Lisa Jöckel, Michael Kläs, Janek Groß et al.

Assurance Cases (ACs) are an established approach in safety engineering to argue quality claims in a structured way. In the context of quality assurance for Machine Learning (ML)-based software components, ACs are also being discussed and appear promising. Tools for operationalizing ACs do exist, yet mainly focus on supporting safety engineers on the system level. However, assuring the quality of an ML component within the system is commonly the responsibility of data scientists, who are usually less familiar with these tools. To address this gap, we propose a framework to support the operationalization of ACs for ML components based on technologies that data scientists use on a daily basis: Python and Jupyter Notebook. Our aim is to make the process of creating ML-related evidence in ACs more effective. Results from the application of the framework, documented through notebooks, can be integrated into existing AC tools. We illustrate the application of the framework on an example excerpt concerned with the quality of the test data.