Public deliverables

D6.4 – Early Plan for the exploitation and dissemination of the results

This document presents the current status on the dissemination, communication and exploitation plans for the project. It contains the list of the current actions and time scales. It details the post-project actions containing the individual partners’ intentions, at the point in time at which the deliverable is published. All the agreements with regard to the ownership of the results and IPR issues will be also included in this document. The outcomes resulting from the Standardisation, Clustering and Concertation tasks will also be reported within the future releases of this deliverable.

This document will have 3 releases:

  • This first release (D6.4), month 12, describing the initial dissemination plan, the list of ongoing and upcoming actions and timelines. It presents a first version of the post-project actions, indicating the intentions of individual partners. We completed this release with a chapter on the risks and their mitigation related to the impact that the current pandemic may have on the realization of our plans.
  • A second release (D6.6), at month 24, presenting the new actions, an update of the communication plan and business plans given that that the first versions of the prototypes will be available, and a market study should be available.
  • A final release (D6.8), at month 36, describing the final exploitation plans of the different consortium members as well as all agreements regarding the ownership of results and IPR issues. The outcomes resulting from the standardisation, clustering and Concertation tasks will also be reported in this final version.
D6.3 – Early Project Presentation and brochure

According to the predefined rules exposed at the beginning of the SmartCLIDE project, project presentation and brochure will be issued in this section. All the images and materials created (Brochure, poster, Roll up for conferences, and project templates) could be downloaded and are, of course, open to use as Creative Commons. This report gives an overview of the SmartCLIDE project public website dissemination area (Dissemination Kit, Blog and Follow up) and internal website and collaboration support.

The public site (www.smartclide.eu) is designed to present the work of the SmartCLIDE project to the general public, the scientific community, and industry. It was already presented with the SmartCLIDE logo on the deliverable D6.3.1.

All partners are collaborating in making local and international news about the goals of the consortium, updating deliverables to the website and keeping the open for public access. Our collaboration infrastructure will be evaluated and upgraded as necessary during the lifetime of the project. All partners are encouraged and reminded regularly to provide additional suggestions and further information regarding activities related to the SmartCLIDE project, so that these can be properly captured and advertised via the project website in order to keep the website current with fresh information and material.

Using the materials provided (printed and online) for their own events and for the events in which the Consortium have presence. (Updated pictures, updated reports, news about the platform, Workshops activities, Interaction with the end users… etc.)

This document will have 3 releases:

  • This first release (D6.3), month 12, describes the first set of assets created for the project.
  • A second release (D6.5), month 24, will present the created assets after 2 years duration
  • A final release (D6.7), month 36, will present the final list of assets created by the project dissemination and communication of the project.
D3.1 – Early SmartCLIDE Cloud IDE Design

Deliverable “D3.1 Early SmartCLIDE IDE Design” is part of WP3 and is produced as the main outcome of Task 3.1 “Design, development and unit testing of the User Interface”, Task 3.2 “Design, development and unit testing of the Deep Learning Engine”, and Task 3.3 “Design, development and unit testing of the Backend Services”. The main objective of WP3 is to design, develop, and unit test the three main SmartCLIDE framework components, namely User Interface (UI), Deep Learning Engine (DLE), and the Backend Components. The purpose of this deliverable is to report the early design approach and technical progress that has been conducted until M20. The emphasis is given on the front and backend of the SmartCLIDE Integrated Development Environment (IDE). This deliverable is based on the outcome of previous WP1 deliverables, namely “D1.4 the SmartCLIDE Concept” and “D1.5 Design of SmartCLIDE Architecture”.  Besides, there is a strong link with WP2, where technology providers have performed research-oriented tasks, which have been documented in “D2.1 SmartCLIDE Innovative Approaches and Features on Services Discovery, Creation, Composition, and Deployment”. This is the first version of deliverable, which is going to be updated in M30.

D1.5 – The SmartCLIDE Architecture

This document presents the architecture of the SmartCLIDE system. It is the result of the design process, strongly dependent on and complementing the defined SmartCLIDE requirements, use cases and conceptual design of its components. Consequently, taking into account both the results of requirements, the specified set of system use cases and pilot scenarios that captured in detail how the envisioned SmartCLIDE system will offer its functionality to the users, and the envisioned technical innovations of the SmartCLIDE system as outlined in its conceptual design, this document focuses on detailing the component-based architecture, the information flows and component interactions view, as well as the deployment architecture of SmartCLIDE. The architecture description is also complemented by the delivery plan of the system – the approach to be used and a time plan for the delivery of the system with specific phases and milestones has been included in the delivery plan. 

All the aforementioned content has been structured following the concept and terms of the ISO/IEC/IEEE 42010:2011, “Systems and software engineering — Architecture description” standard. Although this document does not fully comply with the standard’s requirements, the use of the principles included in the standard increases the standardization of the architecture description and the readability of the document itself. It must be noted that the design process and architecture specification of the system is an ongoing process that will continue in the next phases of the project on the light of new deliverables that are due in the months to follow. Thus, even though the current document, along with D1.3 and D1.4, is a starting point for the specification of the system, modifications will apply in the course of the project in order to record the evolving requirements and the corresponding changes in the architecture specification, following an agile development approach. 

D1.4 – The SmartCLIDE Concept

The current document presents the SmartCLIDE Concept. The work described in this document is part of T1.4 Design of SmartCLIDE System Concept for WP1 – Specification of SmartCLIDE concept and pilot cases.

The objectives of this task can be summarised in the points below:

  • Definition of the features and the functionalities required for the project,
  • Identification of the basic functionality for the SW components that will be developed within this project,
  • Elaboration of the project concept based on the collected requirements.

This document summarises the concept of the envisaged SmartCLIDE solution, based on the requirements of the industrial partners, as well as the state-of-the-art, including the basic approach for each of the main SW services and components.

The main result of these activities is the high-level concept for the SmartCLIDE solution, which will serve as the starting point for the detailed design documents.

D6.2 – Project Website

This report describes the SmartCLIDE project website from its conception to its first adaptation. It explains its current role in the entire dissemination and communication process. It further describes both the methods as well as the technologies used to effectively design and build the SmartCLIDE project website. 

We have further taken advantage of this document to present the other communication channels that we have set up since the start of the project to cover the early dissemination needs of the consortium. 

D6.1 – Open Data Use Plan

This document describes the initial Open Data Use Plan of the SmartCLIDE project and the initial data sets that have been identified to be utilised or to be generated by the four Use Case evaluations for industrial validation of the project technologies. This deliverable also outlines how the research data collected, or generated, will be handled during and after the SmartCLIDE project, describes which methodology for data collection and generation will be followed, and whether and how data will be shared. The data sets are described in accordance with the European Commission guidelines of the Open Research Data Pilot and include the key attributes of data type, format, metadata, use of standards and sharing modalities. 

D1.1 – State-of-the-Art and Market Requirements

The current document constitutes the deliverable D1.1 “State-of-the-Art and Market Analysis” of the SmartCLIDE project. The deliverable aims to explore the current state-of-research and -practice in the topics of interest for the project, and deliver as a main outcome the baseline requirements for the intended framework. The deliverable has been developed using a well-defined strategy, and received contribution from almost all partners of the consortium, so as to provide an as comprehensive view of the current state of the art and market analysis. The deliverable is going to be provided as input to Task 1.2 “Specification of Requirements”. 

Scientific papers
SmartCLIDE: Shortening the Toolchain of SOA-based Cloud Software Development by Automating Service Creation, Composition, Testing, and Deployment

Abstract: Nowadays the majority of cloud applications are developed based on the Service-Oriented Architecture (SOA) paradigm. Large-scale applications are structured as a collection of well-integrated services that are deployed in public, private or hybrid cloud. Despite the inherent benefits that service-based cloud development provides, the process is far from trivial, in the sense that it requires the software engineer to be (at least) comfortable with the use of various technologies in the long cloud development toolchain: programming in various languages, testing tools, build / CI tools, repositories, deployment mechanisms, etc. In this paper, we propose an approach and corresponding toolkit (termed SmartCLIDE-as part of the results of an EU-funded research project) for facilitating SOA-based software development for the cloud, by extending a well-known cloud IDE from Eclipse. The approach aims at shortening the toolchain for cloud development, hiding the process complexity and lowering the required level of knowledge from software engineers. The approach and tool underwent an initial validation from professional cloud software developers. The results underline the potential of such an automation approach, as well as the usability of the research prototype, opening further research opportunities and providing benefits for practitioners.

Services extraction for integration in software projects via an agent-based negotiation system

Abstract:

The great development of the internet and all its associated systems has led to the growth of multiple and diverse capabilities in the field of software development.

One such development was the emergence of code repositories that allow developers to share their projects, as well as allowing other developers to contribute to the growth and improvement of those projects. However, there has been such a growth in the use of these systems that there are multiple works with very similar names and themes that it is not easy to find a repository that completely adapts to the developer’s needs quickly.

This process of searching and researching for repositories that fit the initial needs has become a complex task. Due to the complexity of this process, developers need tools that allow them to process a large amount of information that can be downloaded and analysed programmatically. This complexity can be solved by approaches such as big data and scraping.

This paper presents the design of a data ingestion system for libraries, components and repositories in a multi-agent programming environment.

A template-based approach to code generation within an agent paradigm

Abstract:

Today, the paradigm of multi-agent systems has earned a place in the field of software engineering thanks to its versatility to adapt to various domains. However, the construction of these systems is complex, which leads to additional costs in the implementation process. In recent years, however, several frameworks have emerged to simplify this task by providing functionalities that these systems need as a basis, or even tools to generate code related to this paradigm.  These tools are based on a single framework, protocol and language, which sets many limits to the code generation capacity of these tools.

Therefore, this paper proposes a tool for code generation of complete multi-agent systems, focused on the elimination of the restrictions of programming language, framework, communication protocol, etc. through the use of model-driven and template-driven development.

A Hybrid Supervised/Unsupervised Machine Learning Approach to Classify Web Services

Abstract:

Reusing software is a promising way to reduce software development costs. Nowadays, applications compose available web services to build new software products. In this context, service composition faces the challenge of proper service selection. This paper presents a model for classifying web services. The service dataset has been collected from the well-known public service registry called ProgrammableWeb.

The results were obtained by breaking service classification into a two-step process. First, Natural Language Processing(NLP) pre-processed web service data have clustered by the Agglomerative hierarchical clustering algorithm. Second, several supervised learning algorithms have been applied to determine service categories.

The findings show that the hybrid approach using the combination of hierarchical clustering and SVM provides acceptable results in comparison with other unsupervised/supervised combinations.

Applying Machine Learning in Technical Debt Management: Future Opportunities and Challenges

Abstract: Technical Debt Management (TDM) is a fast-growing field that in the last years has attracted the attention of both academia and industry. TDM is a complex process, in the sense that it relies on multiple and heterogeneous data sources (e.g., source code, feature requests, bugs, developers’ activity, etc.), which cannot be straightforwardly synthesized; leading the community to use mostly qualitative empirical methods. However, empirical studies that involve expert judgment are inherently biased, compared to automated or semi-automated approaches. To overcome this limitation, the broader (not TDM) software engineering community has started to employ machine learning (ML) technologies. Our goal is to investigate the opportunity of applying ML technologies for TDM, through a Systematic Literature Review (SLR) on the application of ML to software engineering problems (since ML applications on TDM are limited).

Thus, we have performed a broader scope study, i.e., on machine learning for software engineering, and then synthesize the results so as to achieve our high-level goal (i.e., possible application of ML in TDM). Therefore, we have conducted a literature review, by browsing the research corpus published in five high-quality SE journals, with the goal of cataloging: (a) the software engineering practices in which ML is used; (b) the machine learning technologies that are used for solving them; and (c) the intersection of the two: developing a problem solution mapping. The results are useful to both academics and industry, since the former can identify possible gaps, and interesting future research directions, whereas the later can obtain benefits by adopting ML technologies.

Articles
SmartCLIDE: Runtime Monitoring and Verification (RMV)

SmartCLIDE offers services to accelerate the creation and deployment of Cloud solutions by providing the ability for non-programmers to construct applications and new services using smart automation. One of the backend services provided by SmartCLIDE is runtime monitoring and verification (RMV) which in conjunction with automated testing is applied to assure the quality of the created services. In this paper we describe the objectives of RMV, and provide an overview of the approach and the benefits.

Increasing Adoption of Cloud Solutions With SmartCLIDE

Abstract: The SmartCLIDE research project aims to bridge the gap between on-demand business strategies and the lack of qualified software professionals by creating a new cloud native IDE that makes it easier to develop and deploy cloud services. The project is funded by the European Union’s Horizon 2020 research and innovation program, and involves a consortium of 11 partners from Germany, Greece, Luxembourg, Portugal, Spain, and the United Kingdom.

Machine Learning for Technical Debt Identification
  • Authors: Dimitrios Tsoukalas, Nikolaos Mittas, Alexandros Chatzigeorgiou, Dionisis D. Kehagias, Apostolos Ampatzoglou, Theodoros Amanatidis, Lefteris Angelis
  • Location: “IEEE Transactions on Software Engineering” journal

Abstract: Technical Debt (TD) is a successful metaphor in conveying the consequences of software inefficiencies and their elimination to both technical and non-technical stakeholders, primarily due to its monetary nature. The identification and quantification of TD rely heavily on the use of a small handful of sophisticated tools that check for violations of certain predefined rules, usually through static analysis. Different tools result in divergent TD estimates calling into question the reliability of findings derived by a single tool. To alleviate this issue we use 18 metrics pertaining to source code, repository activity, issue tracking, refactorings, duplication and commenting rates of each class as features for statistical and Machine Learning models, so as to classify them as High-TD or not. As a benchmark we exploit 18.857 classes obtained from 25 Java projects, whose high levels of TD has been confirmed by three leading tools. The findings indicate that it is feasible to identify TD issues with sufficient accuracy and reasonable effort: a subset of superior classifiers achieved an F<sub>2</sub>-measure score of approximately 0.79 with an associated Module Inspection ratio of approximately 0.10. Based on the results a tool prototype for automatically assessing the TD of Java projects has been implemented.

Presentations

SmartCLIDE presented during the HORIZON CLOUD Community event – March 2022 (Slides)

On 3 March 2022, from 14:00 – 16:00 CET, HORIZON CLOUD hosted its March Community event for the European Cloud Community. The two H2020 Cloud Research and Innovation Actions SmartCLIDE and PHYSICS used the webinar to share their project outcomes with the community.

Enjoy it!

The session has been recorded. Check it out: https://youtu.be/VgmiIp7bGEk

SmartCLIDE presented during the HORIZON CLOUD Community event – March 2022 (Video)

On 3 March 2022, from 14:00 – 16:00 CET, HORIZON CLOUD hosted its March Community event for the European Cloud Community. The two H2020 Cloud Research and Innovation Actions SmartCLIDE and PHYSICS used the webinar to share their project outcomes with the community.

After a brief presentation of the project, we can assist to nice demo on the “SmartCLIDE Service Creation and Composition” component followed by a presentation of the “SmartCLIDE Deep Learning Engine” component.

Enjoy it!

The session has been recorded. Check it out!

SmartCLIDE: Stairway to Cloud (Dec. 2020)

Sebastian Scholze, ATB

Presented at the Open Research Webinars co-organized by the Eclipse Foundation and OW2, Dec. 15, 2020

SmartCLIDE Vision (Nov. 2020)

Presented at the M9 Review

SmartCLIDE Pitch (Oct. 2020)

First public presentation on SmartCLIDE presented during EclipseCon 2020

Videos

SmartCLIDE presented during the HORIZON CLOUD Community event – March 2022 (Video)

On 3 March 2022, from 14:00 – 16:00 CET, HORIZON CLOUD hosted its March Community event for the European Cloud Community. The two H2020 Cloud Research and Innovation Actions SmartCLIDE and PHYSICS used the webinar to share their project outcomes with the community.

After a brief presentation of the project, we can assist to nice demo on the “SmartCLIDE Service Creation and Composition” component followed by a presentation of the “SmartCLIDE Deep Learning Engine” component.

Enjoy it!

The session has been recorded. Check it out!

SmartCLIDE presented at the Open Research Webinars

SmartCLIDE was presented at the Open Research Webinar series, December 15, 2020.

Cloud Computing in a nutshell

This video introduces and summarizes the article Cloud Computing in a nutshell

AGILE Methodologies and DevOps

This video introduces and summarizes the key point of the article AGILE Methodologies and DevOps

An introduction to SmartCLIDE

A four minutes video presenting SmartCLIDE challenges, objectives, and targets

Posters, Flyers & Brochures

SmartCLIDE Poster – Malaga 2021

This poster was originally presented at the FRANSFIERE event.

SmartCLIDE Fact Sheet #1

2-pages introduction to the SmartCLIDE project

Training material

To be defined

Developer resources

GitHub repositories

Currently, all the code developed by the consortium is hosted in the Eclipse Research Labs, a GitHub organization.

You can find all the concerned repositories from the
GitHub SmartCLIDE Team
.

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