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

Logical Foundations of Computer Science, International Symposium, LFCS 2009, Deerfield Beach, FL, USA, January 3-6, 2009. Proceedings

Constraints in form regular expressions over strings are ubiquitous. They occur often in programming languages like Perl and C#, in SQL in form of LIKE expressions, and in web applications. Providing support for regular expression constraints in program analysis and testing has several useful applications. We introduce a method and a tool called Rex, for symbolically expressing and analyzing regular expression constraints. Rex is implemented using the SMT solver Z3, and we provide experimental evaluation of Rex.

/pdf/rex-symbolic-regular-expression-explorer-11azjcg170.pdf

Rex: Symbolic Regular Expression Explorer

Introduction 104
The Story as it is Told from the Business Perspective 104
The Story as it is Told from the Technology Perspective 107Data Challenges 107
Volume 107
Variety, Combining Multiple Data Sets 108
Velocity 108
Veracity, Data Quality, Data Availability 109
Data Discovery 109
Quality and Relevance 109
Data Comprehensiveness 109
Personally Identifiable Information 109
Data Dogmatism 110
Scalability 110Process Challenges 110
Management Challenges 110
Big Data Platforms Technology: Current State of the Art 111Take the Analysis to the Data! 111
What Is Apache Hadoop? 111
Who Are the Hadoop Users? 112
An Example of an Advanced User: Amazon 113
Big Data in Data Warehouse or in Hadoop? 113
Big Data in the Database World (Early 1980s Till Now) 113
Big Data in the Systems World (Late 1990s Till Now) 113
Enterprise Search 115
Big Data “Dichotomy” 115
Hadoop and the Cloud 116
Hadoop Pros 116
Hadoop Cons 116Technological Solutions for Big Data Analytics 118
Scalability and Performance at eBay 122
Unstructured Data 123
Cloud Computing and Open Source 123Every day, 2.5 quintillion bytes of data are created. These data come from 
digital pictures, videos, posts to social media sites, intelligent sensors, purchase transaction records, cell phone GPS signals, to name a few. This is 
known as Big Data.

http://www.odbms.org/wp-content/uploads/2013/07/Big-Data.Zicari.pdf

Big Data: Challenges and Opportunities

Software testing has been commonly used in assuring the quality of database applications. It is often prohibitively expensive to manually write quality tests for complex database applications. Automated test generation techniques, such as Dynamic Symbolic Execution (DSE), have been proposed to reduce human efforts in testing database applications. However, such techniques have two major limitations: (1) they assume that the database that the application under test interacts with is accessible, which may not always be true; and (2) they usually cannot create necessary database states as a part of the generated tests.To address the preceding limitations, we propose an approach that applies DSE to generate tests for a database application. Instead of using the actual database that the application interacts with, our approach produces and uses a mock database in test generation. A mock database mimics the behavior of an actual database by performing identical database operations on itself. We conducted two empirical evaluations on both a medical device and an open source software system to demonstrate that our approach can generate, without producing false warnings, tests with higher code coverage than conventional DSE-based techniques.

/pdf/moda-automated-test-generation-for-database-applications-via-3xozl89xcp.pdf

MODA: automated test generation for database applications via mock objects

We study the problem of generating a database and parameters for a given parameterized SQL query satisfying a given test condition We introduce a formal background theory that includes arithmetic, tuples, and sets, and translate the generation problem into a satisfiability or model generation problem modulo the background theory We use the satisfiability modulo theories (SMT) solver Z3 in the concrete implementation We describe an application of model generation in the context of the database unit testing framework of Visual Studio

/pdf/symbolic-query-exploration-wu2rcc2axk.pdf

Symbolic Query Exploration

A way of combining object-oriented and structural paradigms of software composition is demonstrated in a tool for generative programming. Metaclasses are introduced that are components with specifications called metainterfaces. Automatic code generation is used that is based on structural synthesis of programs. This guarantees that problems of handling data dependencies, order of application of components, usage of higher-order control structures etc are handled automatically. Specifications can be written either in a specification language or given visually on an architectural level. The tool is Java-based and portable.

http://cs.ioc.ee/~pavelg/papers/fse05.pdf

Visual tool for generative programming

The thesis concerns a software development method for constructing programs from models (given by textual as well as visual specifications). The advantage of the method is that once a specification is complete and a computational goal is given, the synthesis of programs can be performed automatically. Flat languages are introduced as a class of declarative languages for specifying single concepts as well as larger systems in a domain-specific manner. Given a problem statement, a program can be obtained from such a specification that computes a required goal. Flat languages are structurally simple, however their expressive power is sufficient to support hierarchy, inheritance and also control structures. Higher-order attribute models are defined to provide the semantics of specifications in flat languages. These attribute models can contain simple and higherorder attribute dependencies. Simple dependencies have attributes as inputs whereas higher-order dependencies also include subtasks that need to be solved during the attribute evaluation. On the one hand, subtasks introduce exponential complexity, on the other hand, they enable us to synthesize recursive, branching and looping programs. We present higher-order attribute evaluation planning algorithm using a concept of maximal linear branches. To synthesize efficient programs, an optimization algorithm is given. A concrete flat language is described, and its semantics is presented by means of higher-order attribute models. The approach is implemented in the software system CoCoViLa that is intended for the development and the usage of domain-specific languages. It adds another powerful dimension to flat languages – visual specification of programs. Specifications are written in Java classes and realizations of attribute dependencies are implemented as Java methods. This enables one to specify software declaratively and also take the advantage of the full power of an imperative language. Algorithm of attribute evaluation on higher-order attribute models can be explained also in terms of logic, and vice versa – theorems of intuitionistic propositional calculus can be encoded in the form of higher-order attribute models. Hence it is interesting to compare the algorithm implemented in CoCoViLa with well-known theorem provers. This is done in the thesis, and the results show that only two of the provers performed better than the CoCoViLa’s algorithms.

Higher-Order Attribute Semantics of Flat Languages

COCOVILA -- Compiler-Compiler for Visual Languages

A flexible Java-based simulation platform that includes both continuous-time and discrete event simulation engines and is intended for applications in a variety of domains is presented. The platform supports visual and model-based software development and uses structural synthesis of programs for translating declarative specifications of simulation problems into executable code. Rich components are an important concept of the work. They are implemented as Java classes with additional specifications for program synthesis, and include visual representations as well as daemons supporting continuous interaction with the user during the simulation. The platform is developed as an open-source software, and its extensions can be written in Java and included into simulation packages.

Pavel Grigorenko

Papers

Symbolic Query Exploration

Visual tool for generative programming

Higher-Order Attribute Semantics of Flat Languages

COCOVILA -- Compiler-Compiler for Visual Languages

CoCoViLa as a multifunctional simulation platform