Spain

Autonomous systems execute complex tasks to perceive the environment and take self-aware decisions with limited human interaction. This autonomy is commonly achieved with the support of Machine Learning algorithms that provide the system with the ability to learn from sensor data. The nature of these algorithms, that need to process large data volumes, poses high-performance demands on the underlying hardware. As a result, the embedded critical real-time domain is adopting increasingly powerful processors that combine multi-core processors with accelerators such as GPUs. The resulting hardware and software complexity makes it difficult to demonstrate that the system will run safely and reliably. This paper tackles part of this challenge by introducing best programming practices and diagnostic mechanisms on the matrix-matrix multiplication function, a central element of existing Machine Learning libraries, in accordance to the recommendations of functional safety standards such as IEC 61508 and ISO 26262. We then evaluate a selection of diagnostic techniques applied to the matrix-matrix multiplication software in the context of integration and execution in high performance multi-core devices. As a result, we provide a catalogue of diagnostic mechanisms with varying degrees of diagnostic coverage for matrix-matrix multiplication execution on heterogeneous high-performance architectures, so that safety engineers can choose the best option for their needs. Moreover, we assess the diagnostic mechanisms on one of the ARM R5 cores of a Zynq UltraScale+ Multi-Processor System-on-Chip and on a Intel i7 processor with native code employing vectorization for the sake of performance.

AEiC 2021 - 25th Ada-Europe International Conference on Reliable Software Technologies

Towards functional safety compliance of matrix-matrix multiplication for machine learning-based autonomous systems.

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### Welcome Message from our Co-Chairs

Through this message, we want to give you our warmest welcome to the 25th Ada-Europe International Conference on Reliable Software Technologies (AEiC 2021). The special circumstances we all go through have made the conference a virtual event. Nonetheless, this will allow us to take advantage of the benefits of this format by using the Underline.io platform that allows remote participation of speakers and attendees and a virtual exhibition.
The platform will also provide a virtual space through Gather.Town to facilitate social interaction among participants, which, in addition to presenting state of the art research results, is one of the most important goals of a conference. This platform will hold various social events together with breaks and a poster session. 

The conference schedule includes a four-day program and exhibition from Monday to Thursday, with parallel tutorials on Monday, and keynotes, technical sessions and a Work-in-Progress session on the remaining three days. The technical sessions include the presentations of 13 journal-track papers, 5 industrial presentations and 6 invited talks on leading topics on reliable software technologies. The program will be completed with three outstanding keynotes that will delight the participants.

The main conference activities will be from 13:00 to 18:00 Central European Summer Time (CEST) to allow participants from America to Central Asia to attend to the conference sessions. Virtual social events are scheduled at the beginning and the end of each day. We encourage you to participate in these activities as well as visiting the exhibition. We look forward to meeting you virtually in June 2021!

**J. Javier Gutiérrez and Mario Aldea Rivas**
 // AEiC 2021 Program Co-Chairs

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technical paper

This talk introduces a formal framework for describing the relationship between a criticality-aware scheduler and a set of application tasks that are assigned different criticality levels. The exposition employs a series of examples starting with scheduling simple jobs and then moving on to mixed-criticality tasks. The proposed formalism extends the rely-guarantee approach, which facilitates formal reasoning about the functional behaviour of concurrent systems, to address real-time properties.

Formally specifying the behaviour of a mixed criticality scheduler

Autonomous vehicles, such as drones, are gaining great popularity due to their usability and versatility. Nowadays, a significant number of them operate using open source software, such as Robot Operating System (ROS) and the de facto standard MAVLink communication protocol, as they are free and many of them are reusable enough that they can be deployed in various different vehicles. Although these technologies offer a wide variety of resources, using them requires a reasonable background of programming and system engineering. Often, this is not achievable by common drone end-users in the short-term, as they would need to acquire a considerably large amount of know-how before working on specific domains. However, a graphical Domain Specific Modelling Language (DSML) might provide a shortcut to design drone missions using already known concepts to the end-users (or, at least, ones easier to learn). Pursuing this shortcut, RoBMEX is presented as a top-down methodology based on a set of domain specific languages able to enhance the autonomy of ROS-based systems, by allowing the creation of missions graphically, and then generating automatically executable source codes conforming to the designed missions.

ROS-based modeling framework for end-users and experts

Early verification and validation (V&V) of design artifacts is one of the main promises of model-based software design. The kind of V&V and possible level of automation depends on the degree of formalization. Semi-formal languages such as UML or SysML facilitate structuring design artifacts and describing them in agreed-upon terms but require additional extension to reach the level of rigour that permits code generation and formal verification. Simulation-oriented languages such as Simulink® and Stateflow® contain sufficient details to allow execution of the model and generation of program code for implementation. In both cases, simulation, formal verification, and code generation require precise definition of language elements. As the goals of and tooling for each of these tasks are different, the tool developers must often define the semantics multiple times and at different levels of detail. This in turn raises the question of whether the language definition used in verification and simulation matches the one used for code generation. This work-in-progress talk will demonstrate model verification with both CodePeer and SPARK Pro tools on simple examples. We will explain how analysis of Ada/SPARK is expected to help C developers. Finally, we cover limitations of the approach and ongoing work.

Using Ada for model verification

Goal modelling aims to capture stakeholder and system goals, together with intentional and structural relationships, in a way that supports trade-off analysis and decision making. Goal models and process models provide complementary and synergetic views of a system, which lead to a more complete understanding of what exists and a better description of what needs to be designed than with only one of these views. The User Requirements Notation (URN), standardized in 2008 by the International Telecommunication Union (ITU-T) with improved versions in 2012 and 2018, combines goal modelling with process modelling, while providing graphical and textual syntaxes for both views. URN helps modellers exploit the value of goal modelling in the context of process and system design and improvement. This presentation provides an overview of URN and its tool support (jUCMNav). Goal/process alignment and traceability analysis to other system design artifacts are also discussed and illustrated. Important benefits and challenges of a goal/process integration for requirements engineering are also discussed.

User Requirements Notation: Beneficial requirements modelling with goals and processes

AADL tools for software architectural design and early verification

Towards functional safety compliance of matrix-matrix multiplication for machine learning-based autonomous systems.

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