The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

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Requirements Engineering A Good Practice Guide

Cloud computing is an emerging technology paradigm that migrates current technological and computing concepts into utility-like solutions similar to electricity and water systems. Clouds bring out a wide range of benefits including configurable computing resources, economic savings, and service flexibility. However, security and privacy concerns are shown to be the primary obstacles to a wide adoption of clouds. The new concepts that clouds introduce, such as multi-tenancy, resource sharing and outsourcing, create new challenges to the security community. Addressing these challenges requires, in addition to the ability to cultivate and tune the security measures developed for traditional computing systems, proposing new security policies, models, and protocols to address the unique cloud security challenges. In this work, we provide a comprehensive study of cloud computing security and privacy concerns. We identify cloud vulnerabilities, classify known security threats and attacks, and present the state-of-the-art practices to control the vulnerabilities, neutralize the threats, and calibrate the attacks. Additionally, we investigate and identify the limitations of the current solutions and provide insights of the future security perspectives. Finally, we provide a cloud security framework in which we present the various lines of defense and identify the dependency levels among them. We identify 28 cloud security threats which we classify into five categories. We also present nine general cloud attacks along with various attack incidents, and provide effectiveness analysis of the proposed countermeasures.

Cloud Computing Security: A Survey

What is knowledge? § is a theoretical or practical understanding of a subject or a domain. § is also the sim of what is currently known, and apparently knowledge is power. Those who possess knowledge are called experts. § Anyone can be considered a domain expert if he or she has deep knowledge and strong practical experience in a particular domain. § The human mental process is internal, and it is too complex to be represented as an algorithm § However, most experts are capable of expressing their knowledge in the form of rules for problem solving.

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Rule‐Based Expert Systems

Model-Driven Development (MDD) emphasizes the use of models at a higher abstraction level in the software development process and argues in favor of automation via model execution, transformation, and code generation. However, one current challenge is how to manage requirements during this process whilst simultaneously stressing the benefits of automation. This paper presents a systematic review of the current use of requirements engineering techniques in MDD processes and their actual automation level. 72 papers from the last decade have been reviewed from an initial set of 884 papers. The results show that although MDD techniques are used to a great extent in platform-independent models, platform-specific models, and at code level, at the requirements level most MDD approaches use only partially defined requirements models or even natural language. We additionally identify several research gaps such as a need for more efforts to explicitly deal with requirements traceability and the provision of better tool support.

A systematic review of the use of requirements engineering techniques in model-driven development

This paper presents a methodology for transforming business designs written in OMG's standard Semantics of Business Vocabulary and Rules (SBVR) framework, into a set of UML models. It involves the transformation of business vocabulary and rules written in SBVR's "Structured English" into a set of UML diagrams, which includes Activity Diagram(AD), Sequence Diagram(SD), and Class Diagram(CD). This transformation works by detecting the distinction between rules which will participate in the construction of Activity Diagram and rules which do not. These rules are imperative in nature. The work in the paper also includes the detection of activities embedded implicitly in those rules and establishment of sequence between those activities. These activities incur some action. We also detect their owner and refer to them as the doer of the action. This plays a very important role in the development of Class Diagrams

/pdf/transformation-of-sbvr-business-design-to-uml-models-24hraz5f0s.pdf

Transformation of SBVR business design to UML models

A simplified description of Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Situation) is presented. We have adapted the TOPSIS description from existing Fuzzy theory literature and distilled the bare minimum concepts required for understanding and applying TOPSIS. An example has been worked out to illustrate the application of TOPSIS for a multi-criteria group decision making scenario.

A Simplified Description of Fuzzy TOPSIS

Software design patterns are solutions to recurring design problems. Analyzing and managing the large and ever increasing number of design patterns is a problem. Non-uniform and incomplete pattern descriptions further complicate the task. Existing literature defines different pattern relationship types and many relationships among patterns. These relationships are analyzed based on designer's experience and their formal basis is unclear. We propose a novel graph based model to capture the semantics of a design pattern using design decisions and their consequences. The relationships are analyzed using various graph properties which enable automation of relationship analysis.

Design decision topology model for pattern relationship analysis

Software design patterns document the most recommended solutions to recurring design problems. Selection of the best design pattern in a given context involves analysis of available alternatives, which is a knowledge-intensive task. Pattern knowledge overload (due to the large number of design patterns) makes such analysis difficult. A knowledge base to generate available alternatives can alleviate the problem. In this paper, we propose a pattern-oriented knowledge model which considers four dimensions of the pattern knowledge space: Pattern to Tactic relationship, Pattern to Pattern relationship, Pattern to Quality-attribute relationship and Pattern to Application-type relationship. We perform analysis of these relationships for patterns in the two popular pattern catalogues viz GoF and POSA1.

/pdf/pattern-oriented-knowledge-model-for-architecture-design-4vuznrqm0k.pdf

Pattern-oriented knowledge model for architecture design

The enterprise data centers and software development teams are increasingly embracing the cloud oriented and virtualized computing platforms and technologies. As a result it is no longer straight forward to choose the most suitable platform which may satisfy a given set of Non-Functional Quality Attributes (NFQA) criteria that is significant for an application. Existing methods such as Serial Evaluation and Consequential Choice [1], [2] etc. are inadequate as they fail to capture the objective measurement of various criteria that are important for evaluating the platform alternatives. In practice, these methods are applied in an ad-hoc fashion. In this paper we introduce three application development platforms: 1) Traditional non-cloud 2) Virtualized and 3) Cloud Aware. We propose a systematic method that allows the stakeholders to evaluate these platforms so as to select the optimal one by considering important criteria. We apply our evaluation method to these platforms by considering a certain (non-business) set of NFQAs. We show that the pure cloud oriented platforms fare no better than the traditional non-cloud and vanilla virtualized platforms in case of most NFQAs.

T. V. Prabhakar

Papers

Transformation of SBVR business design to UML models

A Simplified Description of Fuzzy TOPSIS

Design decision topology model for pattern relationship analysis

Pattern-oriented knowledge model for architecture design

Assessing Suitability of Cloud Oriented Platforms for Application Development