In modern software development, maintaining consistency between architectural documentation and implementation remains a significant challenge. This research explores how large language models (LLMs) can be integrated into an encoder-decoder framework to enable real-time architectural documentation and seamless conversion between semi-formal models (e.g., UML diagrams) and natural language descriptions. Building on previous work in LLM-based requirements verification, we develop a system that automatically converts models into text and vice versa. By allowing LLMs to assess textual descriptions for compliance with requirements, this approach reduces the need for extensive model-side validation through rule-based queries. Additionally, the decoder helps transform text-based process descriptions into structured architectural models, supporting digitalization in organizations. The system’s effectiveness is evaluated by comparing reconstructed models with their originals, assessing how well information is preserved and how accurately the transformations are performed.

Voice User Interfaces (VUI) are particularly useful if the operator has to work hands-free or if her cognitive load is very high.This is the case, e.g., when the operator can be easily disturbed by the environment, the operational task induces stress and there is little or no fault tolerance.However, the factors that contribute to the usefulness of a VUI also complicate the design.Automatic Speech Recognition (ASR) must be robust in noisy environments, under non-optimal microphone conditions and for different types of speech -including stress-induced shouting, hyper articulation and heavy breathing, among others.Commercially available, generic ASR solutions do not fulfil high robustness requirements under these conditions.However, ASR systems can be made robust if they are tailored to their respective use cases and personalised for specific users.This paper introduces a method to customize a Large Vocabulary Continuous Speech Recognizer (LVCSR) system to achieve such robustness.An LVCSR system includes a language model (LM) and an acoustic model (AM).The customization involves adapting both the LM and the AM to the specific operational context.For LM customization, we employ an Use Case Editor (UCE) that provides an intuitive interface, enabling users to align linguistic models with their unique needs.For AM customization, a Multi Speaker Text to Speech Synthesis (MSTTS) module is used to automatically generate personalized speech data, ensuring the model captures the distinctive characteristics of individual speakers.Together, these adaptations ensure the LVCSR system is configured to meet the demands of challenging environments and diverse users.

ILLOD is a tool for detecting abbreviation-expansion pairs (AEPs) in requirement sets. It utilizes syntactic features such as Initial Letters, term Lengths, Order, and Distribution of characters to determine if a term is a potential long form to a given abbreviation. The artifact bundles all source code and data resources to replicate evaluation results presented for ILLOD in two research papers published at the REFSQ2022 Conference and in the Information and Software Technology (IST) journal. In addition, ILLOD can be used to detect AEPs, perform abbreviation detection, and the input data-set can be used for further research in requirements engineering or other related fields. The repository is organized into different directories containing data, Python sources, and notebooks for experiments and evaluations. Detailed instructions are provided to load and use the tool on a local system, and the results generated by ILLOD are stored in output files. The tool demonstrates its effectiveness in detecting AEPs and consolidating glossary terms, and the evaluation results provide insights into the performance of different classifiers. The artifact repository is a valuable resource for researchers and practitioners in the field of requirements engineering and related areas.