D4.2 Policy Briefs 2

The second batch of DITOs policy briefs focuses on four themes: Brief 1 - Environmental sustainability: This brief follows up the policy brief #1 on BioBlitzes and focuses on the pilot study conducted to develop a common evaluation framework for the City Nature Challenge (CNC) 2018 in Europe. Brief 2 - Biodesign: This brief follows up the policy brief #1 on Do It Yourself Biotechnology (DIYBio). It assesses the potential and challenges of biodesign citizen science for education and how it can contribute to achieving the Sustainable Development Goals (SDGs). Brief 3 - RRI indicators that reflect the practices of public engagement organisations: This third brief on the overarching topic of RRI is focussed on enriching the conversation and applications of RRI frameworks, in particular how they can move from being used as tools for assessment by funders and evaluators to being useful guidelines for personal and organisational learning and development. The brief presents results from in-depth conversations with facilitators and insights from reviewing RRI indicators in a way that reflects their practices. Brief 4 - RRI - linking Citizen Science and Open Science: This second policy brief on the topic of RRI is focussed on relations between Citizen Science and Open Science. It draws on initiatives implemented in Europe to identify synergies and future areas of work. In response to request from the mid-term project review for more evidence on inclusion impacts of the project, we have decided to diversify the types of policy briefs we will produce. In addition to ‘classic’ policy briefs aimed at giving an introduction and overview of a given topic (Brief 2 and 4) we now also offer ‘Research Insights’ that are based on gathering more thorough evidence from within the project and providing it to decision-makers (Brief 1 and 3). Like the first batch of briefs, a community-oriented approach was chosen for defining the specific topics of each brief and elaborating the content. Brief 1 has been developed by the ECSA working group on BioBlitzes, Brief 2 in cooperation with the in ECSA working group on Citizen Science for Learning and Education. Brief 3 draws on collaborative evaluation work within the DITOs consortium. Brief 4 was created together with the ECSA working group on Citizen Science and Open Science. The timeline of each policy brief has been adapted to be responsive to schedules of contributors, political dynamics and external demands. Brief 4 was already launched in February 2018. Brief 3 is finished and will be designed and published in the next weeks. Brief 1 and 2 are presented as an advanced draft version. Their final review will be conducted in workshops with the respective working groups and external experts at the International ECSA Conference in Geneva next week. This deliverable concludes the successful second stage of WP4 facilitating policy engagement for RRI. The final batch of policy briefs (M36) will further expand this work on biodesign, environmental sustainability and additional aspects of RRI. DITOs ‘Policy Briefs 2’ is Deliverable 4.2 (D4.2) from the coordination and support action (CSA) Doing It Together science (DITOs), grant agreement 709443

Diagnosis and Molecular Characterization of Chikungunya Virus Infections

In recent years, large-scale outbreaks of chikungunya arbovirus (CHIKV), which is transmitted by the Aedes mosquito, have enabled the rapid propagation of the virus across the world. After acute infection phase with commonly fever, joint pain, headache, or rash, chronic rheumatism (arthralgia or myalgia, anorexia, and concentration disorders) up to 40% of cases is observed. The chronic form is defined by symptoms persisting for more than 3 months, and up to years, after initial diagnosis. Chronic discomfort has been linked to one of the four genotypes described. These genotypes represent different geographic lineages (classification based on partial sequence of viral E1 glycoprotein): West African, East-Central-South-African (ECSA), ECSA-diverged or Indian Ocean Lineage (IOL), and Asian. The first marker detected in CHIK infection is the viral RNA, usually by reverse transcription-polymerase chain reaction (RT-PCR). This marker can be identified in samples within 8 days of symptom onset. The infection can also be diagnosed with serological testing to detect CHIKV-specific immunoglobulin IgG and/or IgM. Sequencing studies can determine the infecting genotype

D1.3 Good Practices in Participatory Biodesign

This report analyses ten event formats from the Doing It Together Science (DITOs) project (1 June 2016 - 31 May 2019). The contribution of this deliverable is to categorise ten DITOs citizen science activity formats into different typologies according to level of engagement: ● Raising awareness: to sensitise the public to social and environmental issues ● Participation: to engage citizens in contributing to scientific projects ● Co-design: to engage a variety of stakeholders in co-creating innovative and interdisciplinary projects. ● We also include the ‘Education’ typology, which engages and empowers young people across all levels of engagement. This taxonomy is intended to help event organisers and scientists pick the relevant citizen science activity formats for their specific goals and help them target particular groups. The report lists the main characteristics of each of the event formats, starting from their objectives, required resources, and showcases DITOs examples of each format as well as discussing their pros and cons. Finally, these event formats are analysed and synthesised to create a set of good practices suggestions for how to use events to engage citizens and society in scientific activities

Towards Automatic Identification of Violation Symptoms of Architecture Erosion

Architecture erosion has a detrimental effect on maintenance and evolution, as the implementation drifts away from the intended architecture. To prevent this, development teams need to understand early enough the symptoms of erosion, and particularly violations of the intended architecture. One way to achieve this, is through the automatic identification of architecture violations from textual artifacts, and particularly code reviews. In this paper, we developed 15 machine learning-based and 4 deep learning-based classifiers with three pre-trained word embeddings to identify violation symptoms of architecture erosion from developer discussions in code reviews. Specifically, we looked at code review comments from four large open-source projects from the OpenStack (Nova and Neutron) and Qt (Qt Base and Qt Creator) communities. We then conducted a survey to acquire feedback from the involved participants who discussed architecture violations in code reviews, to validate the usefulness of our trained classifiers. The results show that the SVM classifier based on word2vec pre-trained word embedding performs the best with an F1-score of 0.779. In most cases, classifiers with the fastText pre-trained word embedding model can achieve relatively good performance. Furthermore, 200-dimensional pre-trained word embedding models outperform classifiers that use 100 and 300-dimensional models. In addition, an ensemble classifier based on the majority voting strategy can further enhance the classifier and outperforms the individual classifiers. Finally, an online survey of the involved developers reveals that the violation symptoms identified by our approaches have practical value and can provide early warnings for impending architecture erosion.Comment: 20 pages, 4 images, 7 tables, Revision submitted to TSE (2023

The state of adoption and the challenges of systematic variability management in industry

Handling large-scale software variability is still a challenge for many organizations. After decades of research on variability management concepts, many industrial organizations have introduced techniques known from research, but still lament that pure textbook approaches are not applicable or efficient. For instance, software product line engineering—an approach to systematically develop portfolios of products—is difficult to adopt given the high upfront investments; and even when adopted, organizations are challenged by evolving their complex product lines. Consequently, the research community now mainly focuses on re-engineering and evolution techniques for product lines; yet, understanding the current state of adoption and the industrial challenges for organizations is necessary to conceive effective techniques. In this multiple-case study, we analyze the current adoption of variability management techniques in twelve medium- to large-scale industrial cases in domains such as automotive, aerospace or railway systems. We identify the current state of variability management, emphasizing the techniques and concepts they adopted. We elicit the needs and challenges expressed for these cases, triangulated with results from a literature review. We believe our results help to understand the current state of adoption and shed light on gaps to address in industrial practice.This work is supported by Vinnova Sweden, Fond Unique Interminist´eriel (FUI) France, and the Swedish Research Council. Open access funding provided by University of Gothenbur

Enabling Real-Time AI Edge Video Analytics

This paper introduces a novel distributed AI model for managing in real-time, edge based intelligent analytics, such as the ones required for smart video surveillance. The novelty relies on distributing the applications in several decomposed functions which are linked together, creating virtual chain func- tions, where both computational and communication limitations are considered. Both theoretical analysis and simulation analysis in a real-case scenario have shown that the proposed model can enable real-time surveillance analytics on a low-cost edge network. Finally, a caching mechanism is proposed and evaluated, reducing further the operational costs of the edge network

Understanding, Analysis, and Handling of Software Architecture Erosion

Architecture erosion occurs when a software system's implemented architecture diverges from the intended architecture over time. Studies show erosion impacts development, maintenance, and evolution since it accumulates imperceptibly. Identifying early symptoms like architectural smells enables managing erosion through refactoring. However, research lacks comprehensive understanding of erosion, unclear which symptoms are most common, and lacks detection methods. This thesis establishes an erosion landscape, investigates symptoms, and proposes identification approaches. A mapping study covers erosion definitions, symptoms, causes, and consequences. Key findings: 1) "Architecture erosion" is the most used term, with four perspectives on definitions and respective symptom types. 2) Technical and non-technical reasons contribute to erosion, negatively impacting quality attributes. Practitioners can advocate addressing erosion to prevent failures. 3) Detection and correction approaches are categorized, with consistency and evolution-based approaches commonly mentioned.An empirical study explores practitioner perspectives through communities, surveys, and interviews. Findings reveal associated practices like code review and tools identify symptoms, while collected measures address erosion during implementation. Studying code review comments analyzes erosion in practice. One study reveals architectural violations, duplicate functionality, and cyclic dependencies are most frequent. Symptoms decreased over time, indicating increased stability. Most were addressed after review. A second study explores violation symptoms in four projects, identifying 10 categories. Refactoring and removing code address most violations, while some are disregarded.Machine learning classifiers using pre-trained word embeddings identify violation symptoms from code reviews. Key findings: 1) SVM with word2vec achieved highest performance. 2) fastText embeddings worked well. 3) 200-dimensional embeddings outperformed 100/300-dimensional. 4) Ensemble classifier improved performance. 5) Practitioners found results valuable, confirming potential.An automated recommendation system identifies qualified reviewers for violations using similarity detection on file paths and comments. Experiments show common methods perform well, outperforming a baseline approach. Sampling techniques impact recommendation performance

Parasol: Efficient Parallel Synthesis of Large Model Spaces

Formal analysis is an invaluable tool for software engineers, yet state-of-the-art formal analysis techniques suffer from well-known limitations in terms of scalability. In particular, some software design domains—such as tradeoff analysis and security analysis—require systematic exploration of potentially huge model spaces, which further exacerbates the problem. Despite this present and urgent challenge, few techniques exist to support the systematic exploration of large model spaces. This paper introduces Parasol, an approach and accompanying tool suite, to improve the scalability of large-scale formal model space exploration. Parasol presents a novel parallel model space synthesis approach, backed with unsupervised learning to automatically derive domain knowledge, guiding a balanced partitioning of the model space. This allows Parasol to synthesize the models in each partition in parallel, significantly reducing synthesis time and making large-scale systematic model space exploration for real-world systems more tractable. Our empirical results corroborate that Parasol substantially reduces (by 460% on average) the time required for model space synthesis, compared to state-of-the-art model space synthesis techniques relying on both incremental and parallel constraint solving technologies as well as competing, non-learning-based partitioning methods