16,364 research outputs found
Hazard Contribution Modes of Machine Learning Components
Amongst the essential steps to be taken towards developing and deploying safe systems with embedded learning-enabled components (LECs) i.e., software components that use ma- chine learning (ML)are to analyze and understand the con- tribution of the constituent LECs to safety, and to assure that those contributions have been appropriately managed. This paper addresses both steps by, first, introducing the notion of hazard contribution modes (HCMs) a categorization of the ways in which the ML elements of LECs can contribute to hazardous system states; and, second, describing how argumentation patterns can capture the reasoning that can be used to assure HCM mitigation. Our framework is generic in the sense that the categories of HCMs developed i) can admit different learning schemes, i.e., supervised, unsupervised, and reinforcement learning, and ii) are not dependent on the type of system in which the LECs are embedded, i.e., both cyber and cyber-physical systems. One of the goals of this work is to serve a starting point for systematizing L analysis towards eventually automating it in a tool
A toolset for the development of mixed-criticality partitioned systems
The development of mixed-criticality virtualized multi-core systems poses new challenges that are being subject of active research work. There is an additional complexity: it is now required to identify a set of partitions, and allocate applications to partitions. In this job, a number of issues have to be considered, such as the criticality level of the application, security and dependability requirements, time requirements granularity, etc. MultiPARTES [11] toolset relies on Model Driven Engineering (MDE), which is a suitable approach in this setting, as it helps to bridge the gap between design issues and partitioning concerns. MDE is changing the way systems are developed nowadays, reducing development time. In general, modelling approaches have shown their benefits when applied to embedded systems. These benefits have been achieved by fostering reuse with an intensive use of abstractions, or automating the generation of boiler-plate code
Migrating to Cloud-Native Architectures Using Microservices: An Experience Report
Migration to the cloud has been a popular topic in industry and academia in
recent years. Despite many benefits that the cloud presents, such as high
availability and scalability, most of the on-premise application architectures
are not ready to fully exploit the benefits of this environment, and adapting
them to this environment is a non-trivial task. Microservices have appeared
recently as novel architectural styles that are native to the cloud. These
cloud-native architectures can facilitate migrating on-premise architectures to
fully benefit from the cloud environments because non-functional attributes,
like scalability, are inherent in this style. The existing approaches on cloud
migration does not mostly consider cloud-native architectures as their
first-class citizens. As a result, the final product may not meet its primary
drivers for migration. In this paper, we intend to report our experience and
lessons learned in an ongoing project on migrating a monolithic on-premise
software architecture to microservices. We concluded that microservices is not
a one-fit-all solution as it introduces new complexities to the system, and
many factors, such as distribution complexities, should be considered before
adopting this style. However, if adopted in a context that needs high
flexibility in terms of scalability and availability, it can deliver its
promised benefits
Semantic Support for Log Analysis of Safety-Critical Embedded Systems
Testing is a relevant activity for the development life-cycle of Safety
Critical Embedded systems. In particular, much effort is spent for analysis and
classification of test logs from SCADA subsystems, especially when failures
occur. The human expertise is needful to understand the reasons of failures,
for tracing back the errors, as well as to understand which requirements are
affected by errors and which ones will be affected by eventual changes in the
system design. Semantic techniques and full text search are used to support
human experts for the analysis and classification of test logs, in order to
speedup and improve the diagnosis phase. Moreover, retrieval of tests and
requirements, which can be related to the current failure, is supported in
order to allow the discovery of available alternatives and solutions for a
better and faster investigation of the problem.Comment: EDCC-2014, BIG4CIP-2014, Embedded systems, testing, semantic
discovery, ontology, big dat
Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design
The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface
Designing Normative Theories for Ethical and Legal Reasoning: LogiKEy Framework, Methodology, and Tool Support
A framework and methodology---termed LogiKEy---for the design and engineering
of ethical reasoners, normative theories and deontic logics is presented. The
overall motivation is the development of suitable means for the control and
governance of intelligent autonomous systems. LogiKEy's unifying formal
framework is based on semantical embeddings of deontic logics, logic
combinations and ethico-legal domain theories in expressive classic
higher-order logic (HOL). This meta-logical approach enables the provision of
powerful tool support in LogiKEy: off-the-shelf theorem provers and model
finders for HOL are assisting the LogiKEy designer of ethical intelligent
agents to flexibly experiment with underlying logics and their combinations,
with ethico-legal domain theories, and with concrete examples---all at the same
time. Continuous improvements of these off-the-shelf provers, without further
ado, leverage the reasoning performance in LogiKEy. Case studies, in which the
LogiKEy framework and methodology has been applied and tested, give evidence
that HOL's undecidability often does not hinder efficient experimentation.Comment: 50 pages; 10 figure
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