Model-Driven Quantum Federated Learning (QFL)

Recently, several studies have proposed frameworks for Quantum Federated Learning (QFL). For instance, the Google TensorFlow Quantum (TFQ) and TensorFlow Federated (TFF) libraries have been deployed for realizing QFL. However, developers, in the main, are not as yet familiar with Quantum Computing (QC) libraries and frameworks. A Domain-Specific Modeling Language (DSML) that provides an abstraction layer over the underlying QC and Federated Learning (FL) libraries would be beneficial. This could enable practitioners to carry out software development and data science tasks efficiently while deploying the state of the art in Quantum Machine Learning (QML). In this position paper, we propose extending existing domain-specific Model-Driven Engineering (MDE) tools for Machine Learning (ML) enabled systems, such as MontiAnna, ML-Quadrat, and GreyCat, to support QFL.Comment: Quantum Programming (QP) 2023 Workshop, Programming 2023, Tokyo, Japa

A Metamodel for Jason BDI Agents

In this paper, a metamodel, which can be used for modeling Belief-Desire-Intention (BDI) agents working on Jason platform, is introduced. The metamodel provides the modeling of agents with including their belief bases, plans, sets of events, rules and actions respectively. We believe that the work presented herein contributes to the current multi-agent system (MAS) metamodeling efforts by taking into account another BDI agent platform which is not considered in the existing platform-specific MAS modeling approaches. A graphical concrete syntax and a modeling tool based on the proposed metamodel are also developed in this study. MAS models can be checked according to the constraints originated from the Jason metamodel definitions and hence conformance of the instance models is supplied by utilizing the tool. Use of the syntax and the modeling tool are demonstrated with the design of a cleaning robot which is a well-known example of Jason BDI architecture

Modelling Contiki-Based IoT Systems

In this paper, we investigate how model-driven engineering (MDE) of Internet of Things (IoT) systems and Wireless-Sensor Networks (WSN) can be supported and introduce a domain-specific metamodel for modeling such systems based on the well-known Contiki operating system. The unique lightweight thread structure of Contiki makes it more preferable in the implementation of new IoT systems instead of many other existing platforms. Although some MDE approaches exist for IoT systems and WSNs, currently there is no study which addresses the modelling according to the specifications of Contiki platform. The work presented in this paper aims at filling this gap and covers the development of both a modeling language syntax and a graphical modeling environment for the MDE of IoTs according to event-driven mechanism and protothread architecture of Contiki. Use of the proposed modeling language is demonstrated with including the development of an IoT system for forest fire detection

Enabling Un-/Semi-Supervised Machine Learning for MDSE of the Real-World CPS/IoT Applications

In this paper, we propose a novel approach to support domain-specific Model-Driven Software Engineering (MDSE) for the real-world use-case scenarios of smart Cyber-Physical Systems (CPS) and the Internet of Things (IoT). We argue that the majority of available data in the nature for Artificial Intelligence (AI), specifically Machine Learning (ML) are unlabeled. Hence, unsupervised and/or semi-supervised ML approaches are the practical choices. However, prior work in the literature of MDSE has considered supervised ML approaches, which only work with labeled training data. Our proposed approach is fully implemented and integrated with an existing state-of-the-art MDSE tool to serve the CPS/IoT domain. Moreover, we validate the proposed approach using a portion of the open data of the REFIT reference dataset for the smart energy systems domain. Our model-to-code transformations (code generators) provide the full source code of the desired IoT services out of the model instances in an automated manner. Currently, we generate the source code in Java and Python. The Python code is responsible for the ML functionalities and uses the APIs of several ML libraries and frameworks, namely Scikit-Learn, Keras and TensorFlow. For unsupervised and semi-supervised learning, the APIs of Scikit-Learn are deployed. In addition to the pure MDSE approach, where certain ML methods, e.g., K-Means, Mini-Batch K-Means, DB-SCAN, Spectral Clustering, Gaussian Mixture Model, Self-Training, Label Propagation and Label Spreading are supported, a more flexible, hybrid approach is also enabled to support the practitioner in deploying a pre-trained ML model with any arbitrary architecture and learning algorithm.Comment: Preliminary versio

A Model-Driven Engineering Technique for Developing Composite Content Applications

Composite Content Applications (CCA) are cross-functional process solutions built on top of Enterprise Content Management systems assembled from pre-built components. Considering the complexity of CCAs, their analysis and development need higher level of abstraction. Model-driven engineering techniques covering the use of Domain-specific Modeling Languages (DSMLs), can provide the abstraction in question by moving software development from code to models which may increase productivity and reduce development costs. Hence, in this paper, we present MDD4CCA, a DSML for developing CCAs. The DSML presents an abstract syntax, a concrete syntax, and an operational semantics, including model-to-model and model-to-code transformations for CCA implementations. Use of the proposed language is evaluated within an industrial case study

Towards model-driven engineering for quantum AI

Over the past decade, Artificial Intelligence (AI) has provided enormous new possibilities and opportunities, but also new demands and requirements for software systems. In particular, Machine Learning (ML) has proven useful in almost every vertical application domain. In the decade ahead, an unprecedented paradigm shift from classical computing towards Quantum Computing (QC), with perhaps a quantum-classical hybrid model, is expected. We argue that the Model-Driven Engineering (MDE) paradigm can be an enabler and a facilitator, when it comes to the quantum and the quantum-classical hybrid applications. This includes not only automated code generation, but also automated model checking and verification, as well as model analysis in the early design phases, and model-to-model transformations both at the design-time and at the runtime. In this paper, the vision is focused on MDE for Quantum AI, particularly Quantum ML for the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS) applications

A model-driven approach to machine learning and software modeling for the IoT

Models are used in both Software Engineering (SE) and Artificial Intelligence (AI). SE models may specify the architecture at different levels of abstraction and for addressing different concerns at various stages of the software development life-cycle, from early conceptualization and design, to verification, implementation, testing and evolution. However, AI models may provide smart capabilities, such as prediction and decision-making support. For instance, in Machine Learning (ML), which is currently the most popular sub-discipline of AI, mathematical models may learn useful patterns in the observed data and can become capable of making predictions. The goal of this work is to create synergy by bringing models in the said communities together and proposing a holistic approach to model-driven software development for intelligent systems that require ML. We illustrate how software models can become capable of creating and dealing with ML models in a seamless manner. The main focus is on the domain of the Internet of Things (IoT), where both ML and model-driven SE play a key role. In the context of the need to take a Cyber-Physical System-of-Systems perspective of the targeted architecture, an integrated design environment for both SE and ML sub-systems would best support the optimization and overall efficiency of the implementation of the resulting system. In particular, we implement the proposed approach, called ML-Quadrat, based on ThingML, and validate it using a case study from the IoT domain, as well as through an empirical user evaluation. It transpires that the proposed approach is not only feasible, but may also contribute to the performance leap of software development for smart Cyber-Physical Systems (CPS) which are connected to the IoT, as well as an enhanced user experience of the practitioners who use the proposed modeling solution

Towards model-driven engineering for quantum AI

Over the past decade, Artificial Intelligence (AI) has provided enormous new possibilities and opportunities, but also new demands and requirements for software systems. In particular, Machine Learning (ML) has proven useful in almost every vertical application domain. In the decade ahead, an unprecedented paradigm shift from classical computing towards Quantum Computing (QC), with perhaps a quantum-classical hybrid model, is expected. We argue that the Model-Driven Engineering (MDE) paradigm can be an enabler and a facilitator, when it comes to the quantum and the quantum-classical hybrid applications. This includes not only automated code generation, but also automated model checking and verification, as well as model analysis in the early design phases, and model-to-model transformations both at the design-time and at the runtime. In this paper, the vision is focused on MDE for Quantum AI, particularly Quantum ML for the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS) applications

Ideal test for android testing: Preliminary work

This paper proposes a hybrid method combining well-known holistic test and mutation testing in software testing for Graphical User Interface (GUI) testing of an android application. Moreover, this hybrid method satisfies requirements of ideal testing that is well known and important in software testing. Presence and absence of GUI based faults are tested within this work experimentally and comparatively in the scale of given or constructed model. First step of the method is modeling the given GUI of android application by Finite State Machine (FSM) and then converting this FSM to Regular Expression (RE). Then, test sequences are generated from a context table that is obtained analysis of the RE model. This process defines first part of the Holistic Testing namely positive testing. In second part called negative testing, the test sequence generation procedure is applied mutants of the FSM obtained after applying selected mutation operators. The generated test sequences from original and mutant models are executed on mutant and original android applications respectively. Test sequences are filtered by using pre-defined selection criteria for both positive and negative testing to achieve ideal test suites that are satisfying requirements of the ideal testing.Bu çalışmanın amacı yazılım testi alanında yaygın olarak kullanılan Bütünsel Test (Holistic Test) ve Mutasyon Testi (Mutation Testing) yöntemlerinin kullanılarak model tabanlı melez bir yöntemin Android uygulamalarının Grafiksel Kullanıcı Arayüz (GKA) testi için öne sürülmesidir. Ayrıca bu melez yöntem test alanında bilinirliği yüksek İdeal Test’in (Ideal Test) gereksinimlerini sağladığı için ayrı bir öneme sahiptir. Öne sürülen melez yöntem sayesinde sistem içindeki kullanıcı arayüz merkezli hataların model ölçeğinde varlığı veya yokluğu, karşılaştırmalı ve deneysel çalışmalar çerçevesinde test edilmiştir. Yöntemin ilk adımı olarak verilen uygulamanın kullanıcı arayüzü bir Sonlu Durum Makinası (SDM) ile modellenmekte ve ardından bu SDM bir Düzenli İfade’ye (Dİ) dönüştürülmektedir. Ardından elde edilen Dİ analizden geçirilerek bağlam tabloları ile ifade edilmekte ve bu tablolar vasıtası ile test dizileri üretilmektedir. Bu işlem pozitif testi tanımlamaktadır. Negatif test için ise aynı işlem SDM’lerden elde edilen mutantlara uygulanmakta ve test dizileri elde edilmektedir. Negatif ve pozitif test için elde edilen test dizileri karşılıklı olarak kod tabalı mutasyonla elde edilen mutantlara ve hatasız sisteme uygulanmaktadır. Test sonuçları tanımlanacak olan test seçim kriterlerine göre bir süzgeçten geçirilmekte ve hem pozitif hemde negatif test için süzgeçten geçirilen test kümeleri elde edilmektedir. Bu işlem sonund

Enabling automated machine learning for model-driven AI engineering

This article presents our work in progress in supporting automated machine learning in the model-driven engineering process of AI-enabled software systems. We argue that the state of practice suffers from two key issues. First, data scientists often follow a trial-and-error process and use certain heuristics to practice machine learning engineering. Therefore, their results are typically far from optimized as we show through an example in this study. Second, software engineers without deep knowledge of machine learning are often required to collaborate with data scientists, integrate and maintain their code, or even take over their tasks due to a general shortage of data scientists worldwide. Hence, there is an urgent need for tools that can support these novice machine learning practitioners. To address the mentioned issues, we deploy the model-driven engineering paradigm and enable automated machine learning in an existing software development methodology and tool that supports this paradigm