65 research outputs found
Model-based (Mechanical) Product Design
No full textLien vers la version éditeur: http://link.springer.com/chapter/10.1007/978-3-642-24485-8_40Mechanical product engineering is a research and industrial activity which studies the design of complex mechanical systems. The process, which involves the collaboration of various experts using domain-specific software, raises syntactic and semantic interoperability issues which are not addressed by existing software solutions or their underlying concepts. This article proposes a flexible model-based software architecture that allows for a federation of experts to define and collaborate in innovative design processes. The presented generic approach is backed and validated by its implementation on an academic usecase
Use of Metamorphic Testing Techniques for Improving Causal Discovery using LLMs
No full textThis thesis explores the use of Large Language Models (LLMs) in causal discovery, a
critical task for understanding complex relationships in data. Traditional methods often
fall short when dealing with high-dimensional datasets and confounders. LLMs, leveraging
metadata and contextual information, offer a novel approach by emulating expert domain
reasoning. To address the inherent biases and limitations of LLMs, this research integrates
metamorphic testing and prompt engineering. Metamorphic testing ensures the reliability
of causal inferences by systematically validating model outputs, while prompt engineering
optimizes input queries for improved accuracy. The study employs datasets such as Asia
and Child to evaluate these methods. Results indicate that combining metamorphic
testing and prompt engineering significantly enhances the robustness and precision of
LLM-based causal discovery, offering a promising direction for future AI applications. This
framework not only advances the theoretical understanding but also provides practical
tools for researchers and practitioners in the field
Toward Sustainable IoT Applications: Unique Challenges for Programming the Batteryless Edge
No full textThe advent of ultra-low-power computer systems has enabled intermittently powered, battery-free devices to operate using harvested ambient energy. We present a roadmap from today’s continuously powered Internet of Things devices to tomorrow’s battery-free devices that highlights challenges for those running intermittent programs.acceptedVersio
Taskify: An Integrated Development Environment to Develop and Debug Intermittent Software for the Batteryless Internet of Things
No full text16th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2020 -- 15 June 2020 through 17 June 2020 -- -- 162821Batteryless embedded devices rely only on ambient energy harvesting that enables stand-alone and sustainable applications for the Internet of Things. These devices perform computation, sensing, and communication when the harvested ambient energy in their energy reservoir is sufficient; they die abruptly when the energy drains out completely. This kind of operation, the so-called intermittent execution, dictates a task-based programming model for the development and implementation of intermittent applications. However, today's task-based intermittent programs are tightly-coupled to the underlying run-time environments. This makes their debugging and testing difficult before deploying them into the target platform. To remedy this, we present Taskify, a tool that enables engineers to write and debug task-based intermittent programs in TaskDSL, i.e., a domain-specific language we designed for the development of intermittent programs on any general-purpose computer. Taskify automatically transforms these programs into C programs that can be linked to the underlying run-time environment and deployed into the target platform. Taskify is implemented as an Eclipse plugin. It has been evaluated on three intermittent applications. © 2020 IEEE
Engineering Carbon Emission-aware Machine Learning Pipelines
No full textThe proliferation of machine learning (ML) has brought unprecedented advancements in technology, but it has also raised concerns about its environmental impact, particularly concerning carbon emissions. To address the imperative of environmentally responsible ML, we present in this paper a novel ML pipeline, named CEMAI, designed to monitor and analyze carbon emissions across the entire lifecycle of ML model development, from data preparation to training and deployment. Our endeavor involves an exhaustive evaluation process underpinned by three industrial case studies. These case studies are structured around the application of ML models to predict tool wear, estimate remaining useful lifetimes, and detect anomalies in the Industrial Internet of Things (IIoT). Leveraging sensor data originating from CNC machining and broaching operations, our research shows empirically the efficacy of carbon emissions as a dependable metric guiding the configuration of an ML development process. The essence of our approach lies in striking a balance between superior performance and minimal carbon emissions. Our findings reveal the potential to optimize pipeline configurations for ML models in a manner that not only enhances performance but also drastically reduces carbon emissions, thereby underlining the significance of adopting ecologically responsible engineering practices.publishedVersio
A Metamodeling Approach for Reasoning on Multiple Requirements Models
No full textInternational audienceThe complex software development projects of today may require developers to use multiple requirements engineering approaches. Different teams may have to use different requirements modeling formalisms to express requirements related to their assigned parts of a given project. This situation poses difficulties in achieving interoperability and integration of requirements models for the purpose of reasoning on the overall system requirements. It is challenging to compose distributed models expressed in different notations and to reason on the composed models. In this paper we present a metamodeling approach which allows reasoning about requirements and their relations on the whole/composed models expressed in different requirements modeling approaches. In a previous work we expressed the structure of requirements documents as a requirements metamodel in which the most important elements are requirements relations and their types. The semantics of these elements is given in First Order Logic (FOL) and allows two activities: inferring new relations from the initial set of relations and checking consistency of relations. In this work we use the requirements metamodel as a core metamodel to be specialized for different requirements modeling approaches and notations such as Product-line and SysML. Mainly, the requirements relations in the metamodel are specialized to support relations in different requirements modeling approaches. The specialization allows using the same semantics and reasoning mechanism of the core metamodel for multiple requirements modeling approaches. To illustrate the approach we use an example from automotive domain expressed with two modeling approaches: product-line requirements models and SysML for system requirements
Railroad Crossing Heterogeneous Model
No full textThis research was supported by ANR GEMOC project.International audienceSystems are getting more and more complex and usually in- volve many stakeholders. Stakeholders are concerned by different aspects of the system, potentially supported by multiple Domain Specific Mod- eling Languages (DSMLs). The DSMLs are usually different not only in their syntax but also in their behavioral semantics. In order to pro- vide simulation and/or verification of the overall system, it is mandatory to compose the DSMLs behavioral semantics. The composition of the DSMLs behavioral semantics results in the coordination of different mod- els that conform to the DSMLs. This paper presents the coordination of the models representing a railroad crossing management system. The sys- tem is composed of two models, conforming to two different DSMLs. The paper explains the behavioral semantics of these DSMLs and presents a simple coordination of the models used in the example
Supporting Change in Product Lines Within the Context of Use Case-driven Development and Testing
No full textProduct Line Engineering (PLE) is a crucial practice in many software development environments where systems are complex and developed for multiple customers with varying needs. At the same time, many business contexts are use case-driven where use cases are the main artifacts driving requirements elicitation and many other development activities. In these contexts, variability information is often not explicitly represented, which leads to ad-hoc change management for use cases, domain models and test cases in product families.
In this thesis, we address the problems of modeling variability in requirements with additional traceability to feature models and the manual and error prone requirements configuration and regression testing in product families.
We provide the following contributions:
- A modeling method for capturing variability information in product line use case and domain models by relying exclusively on commonly used artifacts in use-case driven development, thus avoiding unnecessary modeling overhead.
- An approach for automated configuration of product specific use case and domain models that guides customers in making configuration decisions and automatically generates use case diagrams, use case specifications, and domain models for configured products.
- A change impact analysis approach for evolving configuration decisions in product line use case models that automatically identifies the impact of decision changes on other decisions, and incrementally reconfigures product specific use case diagrams and specifications for evolving decisions.
- An approach for automated classification and prioritization of system test cases in a family of products that automatically classifies and prioritizes, for each new product, system test cases of previous product(s) in a product line, and provides guidance in modifying existing system test cases to cover new use case scenarios that have not been tested in the product line before.
All our approaches have been developed and evaluated in close collaboration with our industry partner IEE
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