Chokri Mraidha

Commissariat à l'Énergie Atomique et aux Énergies Alternatives

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Sébastien Gérard

Res Publica (Norway)

Sara Tucci-Piergiovanni

Commissariat à l'Énergie Atomique et aux Énergies Alternatives

François Terrier

CEA LIST

Jean‐Philippe Babau

Université de Bretagne Occidentale

Ernest Woźniak

Fortiss

Morayo Adedjouma

Commissariat à l'Énergie Atomique et aux Énergies Alternatives

Asma Mehiaoui

Laboratoire d'Informatique et d'Automatique pour les Systèmes

Asma Charfi

Commissariat à l'Énergie Atomique et aux Énergies Alternatives

Rania Mzid

Tunis El Manar University

Mohamed Abid

Embedded Systems (United States)

Cooperative Institutions

Commissariat à l'Énergie Atomique et aux Énergies Alternatives

CEA LIST

Université Paris-Saclay

Centre National de la Recherche Scientifique

CEA Paris-Saclay

Integra (United States)

Embedded Systems (United States)

Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance

Laboratoire d'Informatique et d'Automatique pour les Systèmes

Institut national de recherche en informatique et en automatique

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From User Stories to Models: A Machine Learning Empowered Automation

Takwa Kochbati Shuai Li Sébastien Gérard Chokri Mraidha

In modern software development, manually deriving architecture models from software requirements expressed in natural language becomes a tedious and time-consuming task particularly for more complex systems.Moreover, the increase in size of the developed systems raises the need to decompose the software system into sub-systems at early stages since such decomposition aids to better design the system architecture.In this paper, we propose a machine learning based approach to automatically break-down the system into sub-systems and generate preliminary architecture models from natural language user stories in the Scrum process.Our approach consists of three pillars.Firstly, we compute word level similarity of requirements using word2vec as a prediction model.Secondly, we extend it to the requirement level similarity computation, using a scoring formula.Thirdly, we employ the Hierarchical Agglomerative Clustering algorithm to group the semantically similar requirements and provide an early decomposition of the system.Finally, we implement a set of specific Natural Language Processing heuristics in order to extract relevant elements that are needed to build models from the identified clusters.Ultimately, we illustrate our approach by the generation of sub-systems expressed as UML use-case models and demonstrate its applicability using three case studies.

Heuristics

10.5220/0010197800280040

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Citations (18)

A two-step optimization technique for functions placement, partitioning, and priority assignment in distributed systems

Asma Mehiaoui Ernest Woźniak Sara Tucci-Piergiovanni Chokri Mraidha Marco Di Natale

Modern development methodologies from the industry and the academia for complex real-time systems define a stage in which application functions are deployed onto an execution platform. The deployment consists of the placement of functions on a distributed network of nodes, the partitioning of functions in tasks and the scheduling of tasks and messages. None of the existing optimization techniques deal with the three stages of the deployment problem at the same time. In this paper, we present a staged approach towards the efficient deployment of real-time functions based on genetic algorithms and mixed integer linear programming techniques. Application to case studies shows the applicability of the method to industry-size systems and the quality of the obtained solutions when compared to the true optimum for small size examples.

Processor scheduling

10.1145/2465554.2465572

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Citations (23)

An optimization approach for the synthesis of AUTOSAR architectures

Ernest Woźniak Asma Mehiaoui Chokri Mraidha Sara Tucci-Piergiovanni Sébastien Gérard

Synthesis of automotive architectures is a complex problem that needs an automated support. AUTOSAR, standard for the specification of automotive architectures, defines a synthesis process of software components and their connections in a set of fixed-priority OS tasks distributed over a network of ECUs. During the synthesis process software components are allocated on ECU-s. Since each component encapsulates a set of so-called runnable entities, synthesis completes by partitioning runnable entities in OS tasks with assigned fixed priorities. This paper proposes an optimization approach for the synthesis of AUTOSAR architectures based on genetic algorithms and mixed integer linear programming techniques. Optimization criteria consider end-to-end timing responses and memory consumption.

AUTOSAR

Component (thermodynamics)

10.1109/etfa.2013.6647952

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Citations (31)

An Efficient Modeling and Execution Framework for Complex Systems Development

Isabelle Perseil Laurent Pautet Jean‐François Rolland Mamoun Filali Didier Delanote

In this paper, we present different modeling and execution frameworks that allow us to efficiently analyze, design and verify complex systems, mainly to cope with the specific concerns of the Real-time and embedded systems (RTE) domain. First we depict a UML /MARTE based methodology for executable RTE systems modeling with a framework and its underlying model transformations required to execute UML models conforming to the MARTE standard. The advantages of adopting a more generic action language with formal features are highlighted, in order to raise the level of abstraction with formal features. Then, we investigate how MARTE, with its Time Model facilities, can be made to represent faithfully AADL periodic/aperiodic tasks communicating through event or data ports, in an approach to end-to-end flow latency analysis. An analytical framework allows us to optimize port-based communication by generating a run time executive that utilizes shared data areas where appropriate, while ensuring the timing semantic assumed by the control application. An analysis of the AADL mode change protocol is also provided, exposing a translation process that takes as an input an AADL model and produces as an output a time Petri net. We show how an AADL model transformation provides a formal model for model checking activities and we suggest that model transformation provides useful support to improve the integration of formal verification in an industrial engineering process. As a case study we use an implementation of a UDP /IP protocol stack.

Executable

Model Transformation

Modeling language

Model-driven architecture

10.1109/iceccs.2011.38

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Citations (4)

Enhancing UML Extensions with Operational Semantics.

Lecture notes in computer science (2007)

Arnaud Cuccuru Chokri Mraidha François Terrier Sébastien Gérard

Metamodeling

10.1007/978-3-540-75209-7_19

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Citations (14)

A Model Based System Engineering Methodology for an Autonomous Driving System Design

HAL (Le Centre pour la Communication Scientifique Directe) (2018)

Asma Smaoui Fenglong Liu Chokri Mraidha

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Unifying safe hardware system design and implementation through UML-based architecture description languages

HAL (Le Centre pour la Communication Scientifique Directe) (2018)

Shuai Li Yupanqui Munoz Julho Nataliya Yakymets Asma Charfi Sébastien Gérard

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Extending response-time analysis for the automatic synthesis of functional graphs into fixed-priority distributed systems

Asma Mehiaoui Sara Tucci-Piergiovanni Chokri Mraidha Jean‐Philippe Babau

In this paper, we extend traditional response time analysis to a functional data flow graph (functional graph for brevity) mapped into a set of tasks. Different tasks synchronization semantics can be considered, depending on the way tasks synchronize on inputs consumed and outputs produced by functions in the functional graph. We study three different synchronization semantics and we give the appropriate response time analysis for functional graphs mapped into a set of preemptible fixed-priority tasks distributed over a priority-based network. Our response time analysis extension is needed to integrate response time constraints in an automatic approach for tasks synthesis. In this paper, for each semantics, the corresponding analysis is integrated in a tasks synthesis approach based on mathematical programming. Each semantics is evaluated against the minimum response time that can be obtained running task synthesis over a set of synthetic functional graphs. Finally, different sematics are considered and evaluated for an automotive case study.

Response time

10.1109/sies.2014.6871196

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Citations (2)

Generation of schedulable real-time component implementations

Ansgar Radermacher Chokri Mraidha Sara Tucci-Piergiovanni Sébastien Gérard

In model based approaches for real-time systems (RTS) development, an iterative process that consists in the early stage verification of real-time constraints fulfillment according to a given design is usually performed in order to detect as early as possible unfeasible designs. Schedulability analysis allows this early verification of timing constraints according to so called Schedulability Analysis Model (SAM). This paper proposes a configurable framework for implementation synthesis of component oriented specification that is able to preserve the SAM timing properties that are validated at an early stage of the RTS design.

Component (thermodynamics)

Implementation

Static timing analysis

Real-time operating system

10.1109/etfa.2010.5640987

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Citations (6)

Stream-Based Active Learning with Adaptive Uncertainty and Diversity Thresholds

Prajit T Rajendran Huáscar Espinoza Agnès Delaborde Chokri Mraidha

This paper proposes Stream-based Active Learning method using Adaptive uncertainty and Diversity Thresholds (SALAT), which aims to reduce the amount of data required for labeling in stream-based data settings while accounting for concept drift. The proposed method uses adaptive uncertainty and diversity thresholds, which are monitored by a sliding window of data selection history to ensure that the data selection is neither too conservative nor too aggressive. The effectiveness of SALAT is validated on the MNIST and FashionMNIST datasets, where it achieves similar performance to the baseline with fewer training samples. The experiments also confirm the hypothesis that a fixed threshold approach is deficient, and an adaptive threshold significantly reduces the data requirement to reach an adequate level of performance. The paper shows that SRLRT can handle concept drift effectively, making it applicable to various real-world applications such as text classification, image classification, and speech recognition.

MNIST database

Concept Drift

Sliding window protocol

Baseline (sea)

Adaptive Learning

10.1109/mlsp55844.2023.10285861

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