Computer-assisted learning is widely discussed in the literature to aid the comprehension of SQL queries (Structured Query Language) in higher education. However, it is difficult for educators/instructors to track, monitor and analyze students’ learning situation due to the higher education massification, and institutions with large classes. Consequently, we need to provide for educators a learning dashboard to monitor and analyze the digital traces issued from students during the practice learning in SQL course. We propose a system called LSQL (Learning Analytics for SQL) that is a solution based on the learning analytics’ methodology. To this end, we propose (i) learning environment dedicated to help students understand the syntax and logic of SQL and getting data issued from these students during online SQL lab work, (ii) trace model which is designed to more effectively represent and capture the complex interactions/actions carried by a student during practice learning activities in virtual/remote laboratories, and (iii) learning analytics dashboard for educators to visualize the statistics and metrics that represent the students’ behavior, and control the students progress in SQL skills to enhance the teaching activities. Tool support is fully available.

Cost-based optimization is a promising paradigm that relies on execution queries to enable fast and efficient ex- ecution reached by the database cost model (CM) during query processing/optimization. While a few database management systems (DBMS) already have support for mathematical CMs, developing such a CMs embedded or hard-coded for any DBMS remains a challenging and error-prone task. A generic interface must support a wide range of DBMS independently of the internal structure used for extending and modifying their signature; be efficient for good responsiveness. We propose a solution that provides a common set of parameters and cost primitives allowing intercepting the signature of the internal cost function and changing its internal parameters and configuration options. Therefore, the power of abstraction allows one to capture the designers/develop- ers intent at a higher level of abstraction and encode expert knowledge of domain-specific transformation in order to construct complex CMs, receiving quick feedback as they calibrate and alter the specifications. Our contribution relies on a generic CM interface supported by Model-Driven Engineering paradigm to create cost functions for database operations as intermediate specifications in which more optimization concerning the performance are delegated by our framework and that can be compiled and executed by the target DBMS. A proof-of-concept prototype is implemented by considering the CM that exists in PostgreSQL optimizer.

Summary Recently artificial intelligence techniques in the database community have become a driver for many database applications. The proposed solution adopting AI in the core database shows that incorporating AI improves the query processing and the self‐tuning of database systems. In traditional systems, self‐tuning database systems are commonly addressed with heuristics to suggest the physical structures (e.g., creation of indexes and materialized views) that enable the fastest execution of queries. However, existing designer tools do not explain/justify how the system behaves and the reasoning behind tuning activities. Moreover, these tools do not keep the database administrator (DBA) in the loop of the optimization process to trust some of the automatic tuning decisions. To address this problem, we introduce a framework called Explain‐Tun that enables to predict and explain self‐tuning actions with transparent strategy from historical data using two explicit models, that is, decision tree and random forests. First, we propose AI‐based DBMS to explain how to select physical structures and provide decision rules extracted by machine learning (ML) as a designed plug‐gable component. Second, a goal‐oriented model to keep DBA in the loop of the optimization process in order to manipulate ML models as CRUD entities. Finally, we evaluate our approach on three use cases, results show that bridging the DBA's experience and ML make sense in tuning database systems.

Abstract The complexity of cyber–physical systems (CPSs) is commonly addressed through complex workflows, involving models in a plethora of different formalisms, each with their own methods, techniques, and tools. Some workflow patterns, combined with particular types of formalisms and operations on models in these formalisms, are used successfully in engineering practice. To identify and reuse them, we refer to these combinations of workflow and formalism patterns as modelling paradigms. This paper proposes a unifying (Descriptive) Framework to describe these paradigms, as well as their combinations. This work is set in the context of Multi-Paradigm Modelling (MPM), which is based on the principle to model every part and aspect of a system explicitly, at the most appropriate level(s) of abstraction, using the most appropriate modelling formalism(s) and workflows. The purpose of the Descriptive Framework presented in this paper is to serve as a basis to reason about these formalisms, workflows, and their combinations. One crucial part of the framework is the ability to capture the structural essence of a paradigm through the concept of a paradigmatic structure. This is illustrated informally by means of two example paradigms commonly used in CPS: Discrete Event Dynamic Systems and Synchronous Data Flow. The presented framework also identifies the need to establish whether a paradigm candidate follows, or qualifies as, a (given) paradigm. To illustrate the ability of the framework to support combining paradigms, the paper shows examples of both workflow and formalism combinations. The presented framework is intended as a basis for characterisation and classification of paradigms, as a starting point for a rigorous formalisation of the framework (allowing formal analyses), and as a foundation for MPM tool development.

Mutation testing relies on the principle of artificially injecting faults in systems to create mutants, in order to either assess the sensitivity of existing test suites, or generate test cases that are able to find real faults. Mutation testing has been employed in a variety of application areas and at various levels of abstraction (code and models). In this paper, we focus on model-based mutation testing for timed systems. In order to cartography the field, we provide a taxonomy of mutation operators and discuss their usages on various formalisms, such as timed automata or synchronous languages. We also delineate a research agenda for the field addressing mutation costs, the impact of delays in operators specification and mutation equivalence.

A mogram designates a software language implemented in either a programming or a modelling language. Object-Oriented mograms share many common language features, but also have specificities related to inheritance, collection values, opposite and contained references, or overloading. We propose a mathematical framework that captures the semantics of such mograms with a precise characterisation of the variation points. We implemented a prototype tool that enables formal analysis in a uniform way.

Hidden behind the Internet of Things (IoT), many actors are activelly filling the market with devices and services. From this profusion of actors, a large amount of technologies and APIs, sometimes proprietary, are available, making difficult the interoperability and configuration of systems for IoT technicians. In order to define and manipulate devices deployed in domestic environments, we propose IoTDSL, a Domain-Specific Language meant to specify, assemble and describe the behaviour of interconnected devices. Relying on a high-level rule-based language, users in charge of the deployment of IoT infrastructures are able to describe and combine in a declarative manner structural configurations as well as event-based semantics for devices. This way, language users are freed from technical aspects, playing with high-level representations of devices, while the complexity of the concrete implementation is handled in a dedicated layer where high-level rules are mapped to vendor’s API.

By analogy with software product reuse, the ability to reuse (meta)models and model transformations is key to achieve better quality and productivity. To this end, various opportunistic reuse techniques have been developed, such as higher-order transformations, metamodel adaptation, and model types. However, in contrast to software product development that has moved to systematic reuse by adopting (model-driven) software product lines, we are not quite there yet for modelling languages, missing economies of scope and automation opportunities. Our vision is to transpose the product line paradigm at the metamodel level, where reusable assets are formed by metamodel and transformation fragments and "products" are reusable language building blocks (model types). We introduce featured model types to concisely model variability amongst metamodelling elements, enabling configuration, automated analysis, and derivation of tailored model types. We provide a wish list of software engineering activities to work with featured model types.

In Model Driven Engineering (MDE), models are first-class citizens, and model transformation is MDE's "heart and soul". Since model transformations are executed for a family of conforming models, their validity becomes a crucial issue. This paper proposes to explore the question of the formal verification of model transformation properties through a tri-dimensional approach: the transformation involved, the properties of interest addressed, and the formal verification techniques used to establish the properties. This work allows a better understanding of the expected properties for a particular transformation, and facilitates the identification of the suitable tools and techniques for enabling their verification.