Book ChapterDOI
Cognifying Model-Driven Software Engineering
Jordi Cabot,Robert Clarisó,Marco Brambilla,Sébastien Gérard +3 more
- Vol. 10748, pp 154-160
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TLDR
This vision paper argues that the cognification of MDSE has the potential to reverse the limited adoption of Model-Driven Software Engineering and discusses the opportunities and challenges of cognifying MDSE tasks.Abstract:
The limited adoption of Model-Driven Software Engineering (MDSE) is due to a variety of social and technical factors, which can be summarized in one: its (real or perceived) benefits do not outweigh its costs In this vision paper we argue that the cognification of MDSE has the potential to reverse this situation Cognification is the application of knowledge (inferred from large volumes of information, artificial intelligence or collective intelligence) to boost the performance and impact of a process We discuss the opportunities and challenges of cognifying MDSE tasks and we describe some potential scenarios where cognification can bring quantifiable and perceivable advantages And conversely, we also discuss how MDSE techniques themselves can help in the improvement of AI, Machine learning, bot generation and other cognification techniquesread more
Citations
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Journal ArticleDOI
Grand challenges in model-driven engineering: an analysis of the state of the research
TL;DR: The events brought together experts from industry, academia, and the open-source community to assess what has changed in research in MDE over the last 10 years, what challenges remain, and what new challenges have arisen.
Journal ArticleDOI
Xatkit: A Multimodal Low-Code Chatbot Development Framework
TL;DR: The Xatkit framework is introduced, providing a set of Domain Specific Languages to define chatbots (and voicebots and bots in general) in a platform-independent way and comes with a runtime engine that automatically deploys the chatbot application and manages the defined conversation logic over the platforms of choice.
Proceedings ArticleDOI
An LSTM-Based Neural Network Architecture for Model Transformations
TL;DR: This work proposes to take advantage of the advances in Artificial Intelligence and LSTM to automatically infer model transformations from sets of input-output model pairs, and to autonomously transform new input models into their corresponding output models without the need of writing any transformation-specific code.
Proceedings ArticleDOI
Automated Classification of Metamodel Repositories: A Machine Learning Approach
TL;DR: An experimental evaluation over a dataset of 555 metamodels demonstrates that the technique permits to learn from manually classified data and effectively categorize incoming unlabeled data with a considerably high prediction rate.
Proceedings ArticleDOI
Personalized and automatic model repairing using reinforcement learning
TL;DR: This paper proposes the use of reinforcement learning algorithms to achieve the repair of broken models allowing both automation and personalization and validates this proposal by repairing a large set ofbroken models randomly generated with a mutation tool.
References
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Book ChapterDOI
Process Mining Manifesto
Wil M. P. van der Aalst,Wil M. P. van der Aalst,A Arya Adriansyah,Ana Karla Alves de Medeiros,Franco Arcieri,Thomas Baier,Tobias Blickle,Jagadeesh Chandra Bose,Peter van den Brand,Ronald Brandtjen,Joos C. A. M. Buijs,Andrea Burattin,Josep Carmona,Malu Castellanos,Jan Claes,Jonathan Cook,Nicola Costantini,Francisco Curbera,Ernesto Damiani,Massimiliano de Leoni,Pavlos Delias,Boudewijn F. van Dongen,Marlon Dumas,Schahram Dustdar,Dirk Fahland,Diogo R. Ferreira,Walid Gaaloul,Frank van Geffen,Sukriti Goel,CW Christian Günther,Antonella Guzzo,Paul Harmon,Arthur H. M. ter Hofstede,Arthur H. M. ter Hofstede,John Hoogland,Jon Espen Ingvaldsen,Koki Kato,Rudolf Kuhn,Akhil Kumar,Marcello La Rosa,Fabrizio Maria Maggi,Donato Malerba,RS Ronny Mans,Alberto Manuel,Martin McCreesh,Paola Mello,Jan Mendling,Marco Montali,Hamid Reza Motahari-Nezhad,Michael zur Muehlen,Jorge Munoz-Gama,Luigi Pontieri,Joel Ribeiro,A Anne Rozinat,Hugo Seguel Pérez,Ricardo Seguel Pérez,Marcos Sepúlveda,Jim Sinur,Pnina Soffer,Minseok Song,Alessandro Sperduti,Giovanni Stilo,Casper Stoel,Keith D. Swenson,Maurizio Talamo,Wei Tan,Christopher Turner,Jan Vanthienen,George Varvaressos,Eric Verbeek,Marc Verdonk,Roberto Vigo,Jianmin Wang,Barbara Weber,Matthias Weidlich,Ton Weijters,Lijie Wen,Michael Westergaard,Moe Thandar Wynn +78 more
TL;DR: This manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users to increase the maturity of process mining as a new tool to improve the design, control, and support of operational business processes.
Journal ArticleDOI
Systematic literature review of machine learning based software development effort estimation models
TL;DR: A systematic literature review of empirical studies on ML model published in the last two decades finds that eight types of ML techniques have been employed in SDEE models, and overall speaking, the estimation accuracy of these ML models is close to the acceptable level and is better than that of non-ML models.
Journal ArticleDOI
Algorithm runtime prediction: Methods & evaluation
TL;DR: In this paper, the authors describe extensions and improvements of existing models, new families of models, and a much more thorough treatment of algorithm parameters as model inputs, and comprehensively describe new and existing features for predicting algorithm runtime for propositional satisfiability (SAT), travelling salesperson (TSP), and mixed integer programming (MIP) problems.
Journal ArticleDOI
Researcher Bias: The Use of Machine Learning in Software Defect Prediction
TL;DR: A meta-analysis of all relevant, high quality primary studies of defect prediction to determine what factors influence predictive performance finds that the choice of classifier has little impact upon performance and the major explanatory factor is the researcher group.
Book
Model Driven Engineering and Ontology Development
TL;DR: Stefan Gaevic and his co-authors try to fill this gap by covering the subject of MDA application for ontology development on the Semantic Web by describing existing technologies, tools, and standards like XML, RDF, OWL, MDA, and UML.