Self-healing Multi-Cloud Application Modelling

Cloud computing market forecasts and technology trends confirm that Cloud is an IT disrupting phenomena and that the number of companies with multi-cloud strategy is continuously growing. Cost optimization and increased competitiveness of companies that exploit multi-cloud will only be possible when they are able to leverage multiple cloud offerings, while mastering both the complexity of multiple cloud provider management and the protection against the higher exposure to attacks that multi-cloud brings. This paper presents the MUSA Security modelling language for multi-cloud applications which is based on the Cloud Application Modelling and Execution Language (CAMEL) to overcome the lack of expressiveness of state-of-the-art modelling languages towards easing: a) the automation of distributed deployment, b) the computation of composite Service Level Agreements (SLAs) that include security and privacy aspects, and c) the risk analysis and service match-making taking into account not only functionality and business aspects of the cloud services, but also security aspects. The paper includes the description of the MUSA Modeller as the Web tool supporting the modelling with the MUSA modelling language. The paper introduces also the MUSA SecDevOps framework in which the MUSA Modeller is integrated and with which the MUSA Modeller will be validated.The MUSA project leading to this paper has received funding from the European Union’s Horizon 2020 research and innovation pro- gramme under grant agreement No 644429

Optimising the fit of stack overflow code snippets into existing code

Software developers often reuse code from online sources such as Stack Overflow within their projects. However, the process of searching for code snippets and integrating them within existing source code can be tedious. In order to improve efficiency and reduce time spent on code reuse, we present an automated code reuse tool for the Eclipse IDE (Integrated Developer Environment), NLP2TestableCode. NLP2TestableCode can not only search for Java code snippets using natural language tasks, but also evaluate code snippets based on a user's existing code, modify snippets to improve fit and correct errors, before presenting the user with the best snippet, all without leaving the editor. NLP2TestableCode also includes functionality to automatically generate customisable test cases and suggest argument and return types, in order to further evaluate code snippets. In evaluation, NLP2TestableCode was capable of finding compilable code snippets for 82.9% of tasks, and testable code snippets for 42.9%.Brittany Reid, Christoph Treude, Markus Wagne

Experience Report on the Development of a Specialized Multi-view Multi-stakeholder Model-Based Engineering Framework

International audienc

Business of open source: A case study of integrating existing patterns through narratives

This paper integrates previously published patterns (many of them documented by the author) for open source businesses through narratives. An open source business employs open source as a strategy to strengthen its business model. The paper makes three contributions: it links patterns by creating narratives of how they have been applied by different companies; it documents pattern sequences for each example; and it proposes a way to group the patterns that reflects how strategic open source is to a business

A Model-Driven Solution to Support Smart Mobility Planning

Multimodal journey planners have been introduced with the goal to provide travellers with itineraries involving two or more means of transportation to go from one location to another within a city. Most of them take into account user preferences, their habits and are able to notify travellers with real time traffic information, delays, schedules update, etc.. To make urban mobility more sustainable, the journey planners of the future must include: (1) techniques to generate journey alternatives that take into account not only user preferences and needs but also specific city challenges and local mobility operators resources; (2) agile development approaches to make the update of the models and information used by the journey planners a self-adaptive task; (3) techniques for the continuous journeys monitoring able to understand when a current journey is no longer valid and to propose alternatives. In this paper we present the experiences matured during the development of a complete solution for mobility planning based on model-driven engineering techniques. Mobility challenges, resources and remarks are modelled by corresponding languages, which in turn support the automated derivation of a smart journey planner. By means of the introduced automation, it has been possible to reduce the complexity of encoding journey planning policies and to make journey planners more flexible and responsive with respect to adaptation needs