In this survey, we discuss the challenges of executing scientific workflows as well as existing
Machine Learning (ML) techniques to alleviate those challenges. We provide the context …

Feature modeling is a common approach for configuring and capturing commonalities and
variations among different Software Product Lines (SPL) products. This process is carried …

The result of productive processes is commonly accompanied by a set of justifications which
can be, depending on the product, process-related qualities, traceability documents, product …

The size of Large Language Models (LLMs), and Machine Learning (ML) models in general,
is a key factor of their capacity and quality of their responses. But it comes with a high cost …

Experimental and critical systems are two universes that are more and more tangling
together in domains such as biotechnologies or aeronautics. Verification, Validation and …

The Machine Learning (ML) world is in constant evolution, as the amount of different
algorithms in this context is evolving quickly. Until now, it is the responsibility of data …

Abstract In Software Product Lines (SPLs), multiple design teams work collectively to
configure products. Often, having multiple sub-designs leads to inconsistencies which …

Background. A software product line is a set of software-intensive systems that share a
common, managed set of features satisfying the specific needs of a particular market …

The Machine Learning (ML) community is currently blooming with hundreds of new
algorithms to implement tasks such as data classification for example [1]. To support data …

De nombreux systèmes sont construits aujourd'hui sur la base d'expérimentations à partir
desquelles des connaissances sont apprises et construites. Ces connaissances évoluent en …