IBM Altocumulus: A Cross-Cloud Middleware and Platform

Cloud computing has become the new face of computing and promises to offer virtually unlimited, cheap, readily available, utility type computing resources. Many vendors have entered this market with different offerings ranging from infrastructure-as-a-service such as Amazon, to fully functional platform services such as Google App Engine. However, as a result of this heterogeneity, deploying applications to a cloud and managing them needs to be done using vendor specific methods. This lock in is seen as a major hurdle in adopting cloud technologies to the enterprise. IBM Altocumulus, the cloud middleware platform from IBM Almaden Services Research, aims to solve this very issue of managing applications across multiple clouds. It provides a uniform, service oriented interface to deploy and manage applications in various clouds and also provides facilities to migrate instances across clouds using repeatable best practice patterns. In this demonstration we will present the latest version of the IBM Altocumulus platform and also reveal some of the latest additions on scaling and the ability to perform map-reduce type computations

Zero and low carbon buildings: A driver for change in working practices and the use of computer modelling and visualization

Buildings account for significant carbon dioxide emissions, both in construction and operation. Governments around the world are setting targets and legislating to reduce the carbon emissions related to the built environment. Challenges presented by increasingly rigorous standards for construction projects will mean a paradigm shift in how new buildings are designed and managed. This will lead to the need for computational modelling and visualization of buildings and their energy performance throughout the life-cycle of the building. This paper briefly outline how the UK government is planning to reduce carbon emissions for new buildings. It discusses the challenges faced by the architectural, construction and building management professions in adjusting to the proposed requirements for low or zero carbon buildings. It then outlines how software tools, including the use of visualization tools, could develop to support the designer, contractor and user

Refinement kinds: type-safe programming with practical type-level computation

UID/CEC/04516/2019 PTDC/EEICTP/4293/2014This work introduces the novel concept of kind refinement, which we develop in the context of an explicitly polymorphic ML-like language with type-level computation. Just as type refinements embed rich specifications by means of comprehension principles expressed by predicates over values in the type domain, kind refinements provide rich kind specifications by means of predicates over types in the kind domain. By leveraging our powerful refinement kind discipline, types in our language are not just used to statically classify program expressions and values, but also conveniently manipulated as tree-like data structures, with their kinds refined by logical constraints on such structures. Remarkably, the resulting typing and kinding disciplines allow for powerful forms of type reflection, ad-hoc polymorphism and type-directed meta-programming, which are often found in modern software development, but not typically expressible in a type-safe manner in general purpose languages. We validate our approach both formally and pragmatically by establishing the standard meta-theoretical results of type safety and via a prototype implementation of a kind checker, type checker and interpreter for our language.publishersversionpublishe

Automated metamorphic testing on the analyses of feature models

Copyright © 2010 Elsevier B.V. All rights reserved.Context: A feature model (FM) represents the valid combinations of features in a domain. The automated extraction of information from FMs is a complex task that involves numerous analysis operations, techniques and tools. Current testing methods in this context are manual and rely on the ability of the tester to decide whether the output of an analysis is correct. However, this is acknowledged to be time-consuming, error-prone and in most cases infeasible due to the combinatorial complexity of the analyses, this is known as the oracle problem.Objective: In this paper, we propose using metamorphic testing to automate the generation of test data for feature model analysis tools overcoming the oracle problem. An automated test data generator is presented and evaluated to show the feasibility of our approach.Method: We present a set of relations (so-called metamorphic relations) between input FMs and the set of products they represent. Based on these relations and given a FM and its known set of products, a set of neighbouring FMs together with their corresponding set of products are automatically generated and used for testing multiple analyses. Complex FMs representing millions of products can be efficiently created by applying this process iteratively.Results: Our evaluation results using mutation testing and real faults reveal that most faults can be automatically detected within a few seconds. Two defects were found in FaMa and another two in SPLOT, two real tools for the automated analysis of feature models. Also, we show how our generator outperforms a related manual suite for the automated analysis of feature models and how this suite can be used to guide the automated generation of test cases obtaining important gains in efficiency.Conclusion: Our results show that the application of metamorphic testing in the domain of automated analysis of feature models is efficient and effective in detecting most faults in a few seconds without the need for a human oracle.This work has been partially supported by the European Commission(FEDER)and Spanish Government under CICYT project SETI(TIN2009-07366)and the Andalusian Government project ISABEL(TIC-2533)