Building Enterprise Architecture Agility and Sustenance with SOA

Service-Oriented Architecture(SOA) is primarily regarded as a technical architecture consisting of tools and service specification to build loosely coupled applications. At another level it is also a means to leverage flexibility and agility to system services as it offers a hierarchical framework to coordinate simultaneous business process design and implementations using loosely coupled service infrastructures. SOA has been debated both in the academy and industry and misinterpretations of its nature impede its adoption. We summarize its historical origins and current evolutions. We review technologies that underlie SOA. In particular, we address how to integrate SOA initiatives with current technology platforms, and how to enforce reuse during the design of loosely coupled systems. We also analyze SOA design methodologies and platforms, and what are their roles in the application integration. Finally we outline challenges and future research directions for SOA

GEANT4 : a simulation toolkit

Abstract Geant4 is a toolkit for simulating the passage of particles through matter. It includes a complete range of functionality including tracking, geometry, physics models and hits. The physics processes offered cover a comprehensive range, including electromagnetic, hadronic and optical processes, a large set of long-lived particles, materials and elements, over a wide energy range starting, in some cases, from 250 eV and extending in others to the TeV energy range. It has been designed and constructed to expose the physics models utilised, to handle complex geometries, and to enable its easy adaptation for optimal use in different sets of applications. The toolkit is the result of a worldwide collaboration of physicists and software engineers. It has been created exploiting software engineering and object-oriented technology and implemented in the C++ programming language. It has been used in applications in particle physics, nuclear physics, accelerator design, space engineering and medical physics. PACS: 07.05.Tp; 13; 2

GUISET: A CONCEPTUAL DESIGN OF A GRID-ENABLED PORTAL FOR E-COMMERCE ON-DEMAND SERVICES

Conventional grid-enabled portal designs have been largely influenced by the usual functional requirements such as security requirements, grid resource requirements and job management requirements. However, the pay-as-you-use service provisioning model of utility computing platforms mean that additional requirements must be considered in order to realize effective grid-enabled portals design for such platforms. This work investigates those relevant additional requirements that must be considered for the design of grid-enabled portals for utility computing contexts. Based on a thorough review of literature, we identified a number of those relevant additional requirements, and developed a grid-enabled portal prototype for the Grid-based Utility Infrastructure for SMME-enabling Technology (GUISET) initiative – a utility computing platform. The GUISET portal was designed to cater for both the traditional grid requirements and some of the relevant additional requirements for utility computing contexts. The result of the evaluation of the GUISET portal prototype using a set of benchmark requirements (standards) revealed that it fulfilled the minimum requirements to be suitable for the utility context

Compilation of Heterogeneous Models: Motivations and Challenges

International audienceThe widespread use of model driven engineering in the development of software-intensive systems, including high-integrity embedded systems, gave rise to a "Tower of Babel" of modeling languages. System architects may use languages such as OMG SysML and MARTE, SAE AADL or EAST-ADL; control and command engineers tend to use graphical tools such as MathWorks Simulink/Stateflow or Esterel Technologies SCADE, or textual languages such as MathWorks Embedded Matlab; software engineers usually rely on OMG UML; and, of course, many in-house domain specific languages are equally used at any step of the development process. This heterogeneity of modeling formalisms raises several questions on the verification and code generation for systems described using heterogeneous models: How can we ensure consistency across multiple modeling views? How can we generate code, which is optimized with respect to multiple modeling views? How can we ensure model-level verification is consistent with the run-time behavior of the generated executable application?In this position paper we describe the motivations and challenges of analysis and code generation from heterogeneous models when intra-view consistency, optimization and safety are major concerns. We will then introduce Project P 2 and Hi-MoCo 3-respectively FUI and Eurostars-funded collaborative projects tackling the challenges above. This work continues and extends, in a wider context, the work carried out by the Gene-Auto 4 project [1], [2]. Hereby we will present the key elements of Project P and Hi-MoCo, in particular: (i) the philosophy for the identification of safe and minimal practical subsets of input modeling languages; (ii) the overall architecture of the toolsets, the supported analysis techniques and the target languages for code generation; and finally, (iii) the approach to cross-domain qualification for an open-source, community-driven toolset

How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review

Context: Machine Learning (ML) has been at the heart of many innovations over the past years. However, including it in so-called 'safety-critical' systems such as automotive or aeronautic has proven to be very challenging, since the shift in paradigm that ML brings completely changes traditional certification approaches. Objective: This paper aims to elucidate challenges related to the certification of ML-based safety-critical systems, as well as the solutions that are proposed in the literature to tackle them, answering the question 'How to Certify Machine Learning Based Safety-critical Systems?'. Method: We conduct a Systematic Literature Review (SLR) of research papers published between 2015 to 2020, covering topics related to the certification of ML systems. In total, we identified 217 papers covering topics considered to be the main pillars of ML certification: Robustness, Uncertainty, Explainability, Verification, Safe Reinforcement Learning, and Direct Certification. We analyzed the main trends and problems of each sub-field and provided summaries of the papers extracted. Results: The SLR results highlighted the enthusiasm of the community for this subject, as well as the lack of diversity in terms of datasets and type of models. It also emphasized the need to further develop connections between academia and industries to deepen the domain study. Finally, it also illustrated the necessity to build connections between the above mention main pillars that are for now mainly studied separately. Conclusion: We highlighted current efforts deployed to enable the certification of ML based software systems, and discuss some future research directions.Comment: 60 pages (92 pages with references and complements), submitted to a journal (Automated Software Engineering). Changes: Emphasizing difference traditional software engineering / ML approach. Adding Related Works, Threats to Validity and Complementary Materials. Adding a table listing papers reference for each section/subsection

Search-based system architecture development using a holistic modeling approach

This dissertation presents an innovative approach to system architecting where search algorithms are used to explore design trade space for good architecture alternatives. Such an approach is achieved by integrating certain model construction, alternative generation, simulation, and assessment processes into a coherent and automated framework. This framework is facilitated by a holistic modeling approach that combines the capabilities of Object Process Methodology (OPM), Colored Petri Net (CPN), and feature model. The resultant holistic model can not only capture the structural, behavioral, and dynamic aspects of a system, allowing simulation and strong analysis methods to be applied, it can also specify the architectural design space. Both object-oriented analysis and design (OOA/D) and domain engineering were exploited to capture design variables and their domains and define architecture generation operations. A fully realized framework (with genetic algorithms as the search algorithm) was developed. Both the proposed framework and its suggested implementation, including the proposed holistic modeling approach and architecture alternative generation operations, are generic. They are targeted at systems that can be specified using object-oriented or process-oriented paradigm. The broad applicability of the proposed approach is demonstrated on two examples. One is the configuration of reconfigurable manufacturing systems (RMSs) under multi-objective optimization and the other is the architecture design of a manned lunar landing system for the Apollo program. The test results show that the proposed approach can cover a huge number of architecture alternatives and support the assessment of several performance measures. A set of quality results was obtained after running the optimization algorithm following the proposed framework --Abstract, page iii

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