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. 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: 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. 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. 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. 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”.
-
- Authors: Angeliki-Agathi Tsintzira, Elvira-Maria Arvanitou, Apostolos Ampatzoglou, Alexander Chatzigeorgiou (University of Macedonia)
- Location: Sept. 2020 – 13th International Conference on the Quality of Information and Communications Technology (QUATIC 2020).
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.
Sebastian Scholze, ATB Presented at the Open Research Webinars co-organized by the Eclipse Foundation and OW2, Dec. 15, 2020 Presented at the M9 Review First public presentation on SmartCLIDE presented during EclipseCon 2020
A four minutes video presenting SmartCLIDE challenges, objectives, and targets
2-pages introduction to the SmartCLIDE project
- The Horizon2020 project SmartCLIDE has officially started on 1st January 2020!
- SmartCLIDE: a new cloud-native IDE
- Machine Learning and Deep Learning: A power couple
- Cloud Computing in a nutshell
- Programming By Example
- Service Discovery in a Nutshell
- AGILE methodologies and DevOps
- Use Case: Real-Time Communication Service
- Use Case: Enhance IoT-Catalogue with an integrated Cloud IDE
- Use Case: Provide a Quick Demonstration for a Customer