A type system for components

In modern distributed systems, dynamic reconfiguration, i.e., changing at runtime the communication pattern of a program, is chal- lenging. Generally, it is difficult to guarantee that such modifications will not disrupt ongoing computations. In a previous paper, a solution to this problem was proposed by extending the object-oriented language ABS with a component model allowing the programmer to: i) perform up- dates on objects by means of communication ports and their rebinding; and ii) precisely specify when such updates can safely occur in an object by means of critical sections. However, improper rebind operations could still occur and lead to runtime errors. The present paper introduces a type system for this component model that extends the ABS type system with the notion of ports and a precise analysis that statically enforces that no object will attempt illegal rebinding

HATS Abstract Behavioral Specification: The Architectural View

The Abstract Behavioral Specification (ABS) language is a formal, executable, object-oriented, concurrent modeling language intended for behavioral modeling of complex software systems that exhibit a high degree of variation, such as software product lines. We give an overview of the architectural aspects of ABS: a feature-driven development workflow, a formal notion of deployment components for specifying environmental constraints, and a dynamic component model that is integrated into the language. We employ an industrial case study to demonstrate how the various aspects work together in practic

Optimal and Automated Microservice Deployment: formal definition, implementation and validation of a deployment engine

The main purpose of this work was to study the problem of optimal and automated deployment and reconfiguration (at the architectural level) of microservice systems, proving formal properties and realizing an implemented solution. It started from the Aeolus component model, which was used to formally define the problem of deploying component-based software systems and to prove different results about decidability and complexity. In particular, the Aeolus authors formally prove that, in the general case, such problem is undecidable. Starting from these results we expanded on the analysis of automated deployment and scaling, focusing on microservice architecture. Using a model inspired by Aeolus, considering the characteristics of microservices, we formally proved that the optimal and automated deployment and scaling for microservice architectures are algorithmically treatable. However, the decision version of the problem is NP-complete and to obtain the optimal solution it is necessary to solve an NP-optimization problem. To show the applicability of our approach we decided to also realize a model of a simple but realistic case-study. The model is developed using the Abstract Behavioral Specification (ABS) language, and to calculate the different deployment and scaling plans we used an ABS tool called SmartDepl. To solve the problem, SmartDepl relies on Zephyrus2. Zephyrus2 is a configuration optimizer that allows to compute the optimal deployment configuration of described applications. This work resulted in an extended abstract accepted at the Microservices 2019 conference in Dortmund (Germany), a paper accepted at the FASE 2019 (part of ETAPS) conference in Prague (Czech Republic), and an accepted book chapter

A Hybrid Convolutional Variational Autoencoder for Text Generation

In this paper we explore the effect of architectural choices on learning a Variational Autoencoder (VAE) for text generation. In contrast to the previously introduced VAE model for text where both the encoder and decoder are RNNs, we propose a novel hybrid architecture that blends fully feed-forward convolutional and deconvolutional components with a recurrent language model. Our architecture exhibits several attractive properties such as faster run time and convergence, ability to better handle long sequences and, more importantly, it helps to avoid some of the major difficulties posed by training VAE models on textual data

Integrating model checking with HiP-HOPS in model-based safety analysis

The ability to perform an effective and robust safety analysis on the design of modern safety–critical systems is crucial. Model-based safety analysis (MBSA) has been introduced in recent years to support the assessment of complex system design by focusing on the system model as the central artefact, and by automating the synthesis and analysis of failure-extended models. Model checking and failure logic synthesis and analysis (FLSA) are two prominent MBSA paradigms. Extensive research has placed emphasis on the development of these techniques, but discussion on their integration remains limited. In this paper, we propose a technique in which model checking and Hierarchically Performed Hazard Origin and Propagation Studies (HiP-HOPS) – an advanced FLSA technique – can be applied synergistically with benefit for the MBSA process. The application of the technique is illustrated through an example of a brake-by-wire system

Abstract Meaning Representation for Multi-Document Summarization

Generating an abstract from a collection of documents is a desirable capability for many real-world applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract Meaning Representation (AMR), a semantic representation of natural language grounded in linguistic theory, as a form of content representation. Our approach condenses source documents to a set of summary graphs following the AMR formalism. The summary graphs are then transformed to a set of summary sentences in a surface realization step. The framework is fully data-driven and flexible. Each component can be optimized independently using small-scale, in-domain training data. We perform experiments on benchmark summarization datasets and report promising results. We also describe opportunities and challenges for advancing this line of research.Comment: 13 page