Related papers: Tolerance in Model-Driven Engineering: A Systemati…
Large Language Models (LLMs) have shown impressive capabilities in various applications, but they still face various inconsistency issues. Existing works primarily focus on the inconsistency issues within a single LLM, while we…
This systematic literature review examines the critical challenges and solutions related to scalability and maintainability in Machine Learning (ML) systems. As ML applications become increasingly complex and widespread across industries,…
Within an industrial manufacturing process, tolerancing is a key player. The dimensions uncertainties management starts during the design phase, with an assessment on variability of parts not yet produced. For one assembly step, we can gain…
The term Model-Driven Engineering (MDE) is typically used to describe software development approaches in which abstract models of software systems are created and systematically transformed to concrete implementations. In this paper we give…
Blended modeling is an emerging paradigm involving seamless interaction between multiple notations for the same underlying modeling language. We focus on a model-driven engineering (MDE) approach based on meta-models to develop textual…
Robust control is a core approach for controlling systems with performance guarantees that are robust to modeling error, and is widely used in real-world systems. However, current robust control approaches can only handle small system…
Behavioral models play an essential role in Model-driven engineering (MDE). Keeping inter-related behavioral models consistent is critical to use them successfully in MDE. However, consistency checking for behavioral models, especially in a…
We present a prototype of a tool leveraging the synergy of model driven engineering (MDE) and Large Language Models (LLM) for the purpose of software development process automation in the automotive industry. In this approach, the…
The replication mechanism resolves some challenges with big data such as data durability, data access, and fault tolerance. Yet, replication itself gives birth to another challenge known as the consistency in distributed systems.…
Context: Software testing plays an essential role in product quality improvement. For this reason, several software testing models have been developed to support organizations. However, adoption of testing process models inside…
Systematic literature reviews (SLR) have been increasingly conducted in software engineering and they provide significant benefits in terms of summarizing the state of the research. The process of conducting SLR is complex, involving…
While Machine Learning (ML) technologies are widely adopted in many mission critical fields to support intelligent decision-making, concerns remain about system resilience against ML-specific security attacks and privacy breaches as well as…
In the last couple of years, Model Driven Engineering (MDE) gained a prominent role in the context of software engineering. In the MDE paradigm, models are considered first level artifacts which are iteratively developed by teams of…
Sensor-driven systems are increasingly ubiquitous: they provide both data and information that can facilitate real-time decision-making and autonomous actuation, as well as enabling informed policy choices by service providers and…
Deep Learning of neural networks has progressively become more prominent in healthcare with models reaching, or even surpassing, expert accuracy levels. However, these success stories are tainted by concerning reports on the lack of model…
Model-Driven Engineering (MDE) has seen significant advancements with the integration of Machine Learning (ML) and Deep Learning (DL) techniques. Building upon the groundwork of previous investigations, our study provides a concise overview…
Fault-tolerant distributed algorithms are central for building reliable spatially distributed systems. Unfortunately, the lack of a canonical precise framework for fault-tolerant algorithms is an obstacle for both verification and…
Normative requirements specify social, legal, ethical, empathetic, and cultural (SLEEC) norms that must be observed by a system. To support the identification of SLEEC requirements, numerous standards and regulations have been developed.…
Supercomputing systems today often come in the form of large numbers of commodity systems linked together into a computing cluster. These systems, like any distributed system, can have large numbers of independent hardware components…
The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned…