Visual Contracts as Test Oracle in AGG 2.0

A test oracle predicts expected outcomes for a set of test cases, often based on a formal, executable specification. Visual contracts are graph transformation rules describing pre- and post-conditions of a service’s operations. To obtain an oracle based on visual contracts, we use the Attributed Graph Grammar System (AGG) to execute the rules, creating a simulation of the behaviour expected of the system under test.The paper discusses the basic idea, illustrates it by an example, describes the challenges and solutions of its implementation and draws conclusions for the use of graph transformation and AGG in test oracles

Metamodel-based model conformance and multiview consistency checking

Model-driven development, using languages such as UML and BON, often makes use of multiple diagrams (e.g., class and sequence diagrams) when modeling systems. These diagrams, presenting different views of a system of interest, may be inconsistent. A metamodel provides a unifying framework in which to ensure and check consistency, while at the same time providing the means to distinguish between valid and invalid models, that is, conformance. Two formal specifications of the metamodel for an object-oriented modeling language are presented, and it is shown how to use these specifications for model conformance and multiview consistency checking. Comparisons are made in terms of completeness and the level of automation each provide for checking multiview consistency and model conformance. The lessons learned from applying formal techniques to the problems of metamodeling, model conformance, and multiview consistency checking are summarized

Semantic model-driven development of service-centric software architectures

Service-oriented architecture (SOA) is a recent architectural paradigm that has received much attention. The prevalent focus on platforms such as Web services, however, needs to be complemented by appropriate software engineering methods. We propose the model-driven development of service-centric software systems. We present in particular an investigation into the role of enriched semantic modelling for a modeldriven development framework for service-centric software systems. Ontologies as the foundations of semantic modelling and its enhancement through architectural pattern modelling are at the core of the proposed approach. We introduce foundations and discuss the benefits and also the challenges in this context

An Embedded Domain Specific Language to Model, Transform and Quality Assure Business Processes in Business-Driven Development

In Business-Driven Development (BDD), business process models are produced by business analysts. To ensure that the business requirements are satisfied, the IT solution is directly derived through a process of model refinement. If models do not contain all the required technical details or contain errors, the derived implementation would be incorrect and the BDD lifecycle would have to be repeated. In this project we present a functional domain specific language embedded in Haskell, with which: 1) models can rapidly be produced in a concise and abstract manner, 2) enables focus on the specifications rather than the implementation, 3) ensures that all the required details, to generate the executable code, are specified, 4) models can be transformed, analysed and interpreted in various ways, 5) quality assures models by carrying out three types of checks; by Haskell.s type checker, at construction-time and by functions that analyse the soundness of models, 6) enables users to define quality assured composite model transformations

Test Case Generation Using Visual Contracts

Visual contracts provide a diagrammatic notation for pre- and postconditionsas alternative to the Object-Constraint Language (OCL) or code-levelcontract languages. Using visual contracts for testing, we benefit from their executabilityand formal background in graph transformation to provide model-basedtest oracles and coverage criteria. Based on a static analysis of their dependenciesand conflicts, in this paper we use visual contracts to generate test cases accordingto these coverage criteria.Together with previous work, this adds up to a comprehensive approach aiming toautomate the three major challenges of testing through the use of models

Automated verification of model transformations based on visual contracts

The final publication is available at Springer via http://dx.doi.org/10.1007/s10515-012-0102-yModel-Driven Engineering promotes the use of models to conduct the different phases of the software development. In this way, models are transformed between different languages and notations until code is generated for the final application. Hence, the construction of correct Model-to-Model (M2M) transformations becomes a crucial aspect in this approach. Even though many languages and tools have been proposed to build and execute M2M transformations, there is scarce support to specify correctness requirements for such transformations in an implementation-independent way, i.e., irrespective of the actual transformation language used. In this paper we fill this gap by proposing a declarative language for the specification of visual contracts, enabling the verification of transformations defined with any transformation language. The verification is performed by compiling the contracts into QVT to detect disconformities of transformation results with respect to the contracts. As a proof of concept, we also report on a graphical modeling environment for the specification of contracts, and on its use for the verification of transformations in several case studies.This work has been funded by the Austrian Science Fund (FWF) under grant P21374-N13, the Spanish Ministry of Science under grants TIN2008-02081 and TIN2011-24139, and the R&D programme of the Madrid Region under project S2009/TIC-1650

R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections

The influence of Deep Learning on image identification and natural language processing has attracted enormous attention globally. The convolution neural network that can learn without prior extraction of features fits well in response to the rapid iteration of Android malware. The traditional solution for detecting Android malware requires continuous learning through pre-extracted features to maintain high performance of identifying the malware. In order to reduce the manpower of feature engineering prior to the condition of not to extract pre-selected features, we have developed a coloR-inspired convolutional neuRal networks (CNN)-based AndroiD malware Detection (R2-D2) system. The system can convert the bytecode of classes.dex from Android archive file to rgb color code and store it as a color image with fixed size. The color image is input to the convolutional neural network for automatic feature extraction and training. The data was collected from Jan. 2017 to Aug 2017. During the period of time, we have collected approximately 2 million of benign and malicious Android apps for our experiments with the help from our research partner Leopard Mobile Inc. Our experiment results demonstrate that the proposed system has accurate security analysis on contracts. Furthermore, we keep our research results and experiment materials on http://R2D2.TWMAN.ORG.Comment: Verison 2018/11/15, IEEE BigData 2018, Seattle, WA, USA, Dec 10-13, 2018. (Accepted

A survey of software development practices in the New Zealand software industry

We report on the software development techniques used in the New Zealand software industry, paying particular attention to requirements gathering. We surveyed a selection of software companies with a general questionnaire and then conducted in-depth interviews with four companies. Our results show a wide variety in the kinds of companies undertaking software development, employing a wide range of software development techniques. Although our data are not sufficiently detailed to draw statistically significant conclusions, it appears that larger software development groups typically have more well-defined software development processes, spend proportionally more time on requirements gathering, and follow more rigorous testing regimes