Automation engineering in process industries involves mapping IO list specifications for sensor and actuator signals to control function blocks from vendor libraries. Due to the non-standardized format and content of IO lists, it is challenging to automate this mapping as it involves interpreting unstructured data (e.g., signal descriptions). Previous approaches proposed mapping of IO list contents to common data schemas, such as ontologies or UML models, but did not provide means to automate the initial data import from heterogeneous input formats. We propose IO-AutoMapper, an LLM-supported method to automatically process spreadsheet IO lists and map their entries to control library function blocks. Experiments with five IO lists with 300 signals each showed more than 98% correct mappings and low LLM processing costs. While LLM hallucinations could not yet be fully eliminated, the method has the potential to reduce human labor for mapping IO list entries by more than 90%.

Coping with different and changing requirements leads to concurrent products (variability in space) and subsequent revisions (variability in time). Moreover, products consist of interrelated models that represent different kinds of artifacts. Dependencies and redundancies between interrelated models within a product and across products can quickly lead to inconsistencies during evolution. Thus, dealing with both variability dimensions uniformly while preserving consistency of interrelated models is a major challenge when developing large and long-living variable systems. Recent research addresses uniform management of variability in space and time by unifying concepts and operations from software product line engineering and software configuration management. However, consistency preservation for interrelated models, which is a major research topic in model-driven software development, has hardly been considered in variability management. We propose an approach that builds on recent efforts for unifying variability in space and time and leverages view-based consistency preservation for systems comprised of different kinds of interrelated models. We evaluate our approach by applying it to two real-world case studies: the well-known ArgoUML-SPL, that is based on the UML modeling tool ArgoUML, and MobileMedia, a mobile application for media management. Our results show that, by manually evolving only the Java models of products, other interrelated models (i.e.,~UML class diagrams) and the remaining affected products can be kept consistent fully automatically.

Abstract With the increasing demand for customized systems and rapidly evolving technology, software engineering faces many challenges. A particular challenge is the development and maintenance of systems that are highly variable both in space (concurrent variations of the system at one point in time) and time (sequential variations of the system, due to its evolution). Recent research aims to address this challenge by managing variability in space and time simultaneously. However, this research originates from two different areas, software product line engineering and software configuration management, resulting in non-uniform terminologies and a varying understanding of concepts. These problems hamper the communication and understanding of involved concepts, as well as the development of techniques that unify variability in space and time. To tackle these problems, we performed an iterative, expert-driven analysis of existing tools from both research areas to derive a conceptual model that integrates and unifies concepts of both dimensions of variability. In this article, we first explain the construction process and present the resulting conceptual model. We validate the model and discuss its coverage and granularity with respect to established concepts of variability in space and time. Furthermore, we perform a formal concept analysis to discuss the commonalities and differences among the tools we considered. Finally, we show illustrative applications to explain how the conceptual model can be used in practice to derive conforming tools. The conceptual model unifies concepts and relations used in software product line engineering and software configuration management, provides a unified terminology and common ground for researchers and developers for comparing their works, clarifies communication, and prevents redundant developments.

The development of variable software, in general, and feature models, in particular, is an error-prone and time-consuming task. It gets increasingly more challenging with industrial-size models containing hundreds or thousands of features and constraints. Each change may lead to anomalies in the feature model such as making some features impossible to select. While the detection of anomalies is well-researched, giving explanations is still a challenge. Explanations must be as accurate and understandable as possible to support the developer in repairing the source of an error. We propose an efficient and generic algorithm for explaining different anomalies in feature models. Additionally, we achieve a benefit for the developer by computing short explanations expressed in a user-friendly manner and by emphasizing specific parts in explanations that are more likely to be the cause of an anomaly. We provide an open-source implementation in FeatureIDE and show its scalability for industrial-size feature models.