Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least 4 m. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the 6.5 m James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 yr, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.

The microservices architectural style has been increasingly adopted in recent years due to its advantageous characteristics. While there exist design patterns for microservices that are theoretically beneficial for ensuring performance (and anti-patterns that should be avoided), they do not always lead to the expected impact. Some designs are solutions for achieving quality properties other than performance, and while they might be categorized as anti-patterns in theory, their influence on system performance can be minimal, depending on the context. Architects find it hard to reason about these trade-offs and the impact that design decisions will have on performance before implementing them. To solve this problem, in this paper, we propose an approach that enables rapid evaluation of high-level architectural models by combining both static and dynamic analysis. The static analysis identifies the design anti-patterns that are known to hinder system performance and guides the architect in reasoning about these design decisions. The approach then generates source code for the system under study based on the architectural design model for a subsequent dynamic analysis to assess whether the statically detected anti-patterns do indeed have a negative effect on the performance of the analyzed system.

Artificial intelligence (AI) is a very disruptive technology, which combined with powerful computational hardware have opened new possibilities for world-wide technological progress in industry. There is an urgent need for systematic development and implementation of AI to see its real impact in the next generation of industrial systems, namely Industry 4.0. Organisations that do not do so will fail to maintain their competitiveness. This paper provides an insight into the main paradigms of AI technologies used in Industry 4.0, by giving emphasis to the key enabling digitalization technologies and their challenges. In addition, we present an overview of AI current state and the most important AI algorithms used in Industry 4.0. Finally, we discuss trends related to adoption of AI in the context of software embedded applications and software architectures for embedded systems.

Vještačka inteligencija (VI) je veoma disruptivna tehnologija, koja u kombinaciji sa snažnim hardverom za procesiranje otvara mogućnosti za opšti napredak u industriji. Postoji hitna potreba za sistemskim razvojem i implementacijom VI radi njenog učinka u industrijskim sistemima, posebno u četvrtoj industrijskoj revoluciji (Industrija 4.0). Tržišni subjekti koji ne usvoje VI neće biti u mogućnostiodržati svoju konkurentnost na tržištu. Ova publikacija pruža uvid u glavne paradigme VI korištene u Industriji 4.0, stavljajući naglasak na ključne digitalne tehnologije i njihove izazove. Pored toga, u ovoj publikaciji smo napravili pregled trenutnog stanja u VI, te pregled najvažnijih algoritama korištenih u Industriji 4.0. Pored navedenih tema, u ovoj publikaciji diskutujemo i o trendovima vezanim za usvajanje VI u kontekstu ugradbenih aplikacija i softverskih arhitektura uopšteno.

Architectural patterns are solutions to common problems in software design. These usually tackle one or more customer requirements (business, functional, or quality requirements). As continuous deployment becomes more important for satisfying customer’s experience in software projects, there is a greater need in supporting continuous integration as its prerequisite. The nature of architectural design patterns is that some are more suitable for continuous integration then others. However, the existing approaches do not prescribe how to tailor architectural design considering integration challenges.To identify patterns suitable for continuous integration, we present a methodology for identification of architectural design patterns that support continuous integration based on continuous assessment. Our methodology, based on Aglie, enables DevOps engineers to assess their integration experience considering design changes, implementation changes, and creation of new version deliverables. The methodology complements common DevOps activities and introduces templates for communicating feedback from DevOps engineers to architects. Architects handle the communicated feedback as requirements and optimise their design accordingly. In this way, the design decisions take shape of solution patterns that, besides other existing requirements, satisfy requirements necessary to facilitate continuous integration.

Concurrent multithreaded programs are more complex than sequential ones due to inter-dependencies of threads over shared memory. Because of these, software architects and developers quickly become overwhelmed when trying to design and manage concurrent software. Existing approaches that try to support architecture efforts in this domain rely on the visualization of concurrency-related properties of software to ease its understanding, but they fail because i) the abstractions they use do not capture information of architectural significance, and because ii) raw visualization of the interdependencies does not scale. In this paper, we suggest a scalable solution that focuses on the abstraction of concurrency properties and their visualization using architectural views. Our framework for automatic extraction of concurrency-related architectural properties (ArchViMP) proposes i) a set of logical rules that abstract concurrency-related architecturally significant software properties and ii) a set of architectural views suitable for showing these concurrency properties.

Software development ecosystems vary significantly among different industrial domains. Therefore, it is challenging to establish quality assurance processes that can be deployed seamlessly to multiple domains. In this paper, we extend our previous work on performance and scalability assessment by identifying the architecture variability points in our PPTAM tooling infrastructure. The goal is to design a modifiable software architecture that enables low cost deployment of our performance and scalability assessment approach. We present the scalability assessment context, architecture modifiability, and lessons learned that were derived from our experience with scalability assessment in several business domains. Specifically, we describe our experience with the application of the proposed approach to a large complex telecom system at Ericsson.

Adopting AI as a technology that solves a particular problem (i.e., meets an architectural driver) is a significant architectural decision. Existing techniques for adequacy assessment of architectural decisions often fail to predict effects of adopting complex technologies such is AI. In this paper, we argue that the reason for this is that they fail to capture the level of knowledge that architects have about a complex technology that they aim to adopt, making the discussion about it difficult and adequacy check prone to mistakes. To solve these issues, we introduce an approach that instructs architects to decompose complex drivers for adopting new technologies according to properties of the technology, and to explicitly assess knowledge that architects have about each of those properties. In order to do so, we present a template that explicitly captures the level of knowledge that architects have about important AI properties, which serve as new requirements exposing the influence of adopting AI on software system. Through evaluation, we have demonstrated that our approach successfully complements existing adequacy assessment techniques and is able to expose influences of adopting new complex technologies on underlying software architecture.

Manufacturers of automated systems and their components have been allocating an enormous amount of time and effort in R&D activities. This effort translates into an overhead on the V&V (verification and validation) process making it time-consuming and costly. In this paper, we present an ECSEL JU project (VALU3S) that aims to evaluate the state-of-the-art V&V methods and tools, and design a multi-domain framework to create a clear structure around the components and elements needed to conduct the V&V process. The main expected benefit of the framework is to reduce time and cost needed to verify and validate automated systems with respect to safety, cyber-security, and privacy requirements. This is done through identification and classification of evaluation methods, tools, environments and concepts for V&V of automated systems with respect to the mentioned requirements. To this end, VALU3S brings together a consortium with partners from 10 different countries, amounting to a mix of 25 industrial partners, 6 leading research institutes, and 10 universities to reach the project goal.

Smart Ecosystem reflects in the control decisions of entities of different nature, especially of its software components. Particularly, the malicious behavior requires a more accurate attention. This paper discusses the challenges related to the evaluation of software smart agents and proposes a first solution leveraging the monitoring facilities for a) assuring conformity between the software agent and its digital twin in a real-time evaluation and b) validating decisions of the digital twins during runtime in a predictive simulation.

This paper presents a semi-automated approach for detecting conflicts and inconsistencies between architecture solutions. Inconsistencies occur when two or more architecture solutions rely on each other but cannot be satisfied, and conflicts when there are contradictions within one single decision specification, such as contradictory variable range and value. The proposed approach comprises a set of checks followed by transformations of architecture solutions specified according to a domain-specific-language, also created in the context of this work, into state machines. The semi-automated approach is implemented as a plugin for the MagicDraw modeling tool, and was evaluated in a project from the automotive domain.