scholarly journals Metamodeling for Medical Devices: Code Generation, Model-debugging and Run-time Synchronization

2015 ◽  
Vol 63 ◽  
pp. 539-544 ◽  
Author(s):  
Juha-Pekka Tolvanen ◽  
Verislav Djukić ◽  
Aleksandar Popovic
Keyword(s):  
Medical Devices ◽  
Code Generation ◽  
Time Synchronization ◽  
Generation Model ◽  
Run Time

Routine run-time code generation

2003 ◽  
Vol 38 (12) ◽  
pp. 44-56 ◽  
Author(s):  
Sam Kamin
Keyword(s):  
Code Generation ◽  
Time Code ◽  
Run Time

scholarly journals Insertion-based Decoding with Automatically Inferred Generation Order

2019 ◽  
Vol 7 ◽  
pp. 661-676 ◽  
Author(s):  
Jiatao Gu ◽  
Qi Liu ◽  
Kyunghyun Cho
Keyword(s):  
Machine Translation ◽  
Real World ◽  
Word Order ◽  
Code Generation ◽  
State Of The Art ◽  
Generation Model ◽  
Beam Search ◽  
Input Information ◽  
Sequence Generation ◽  
Image Caption

Conventional neural autoregressive decoding commonly assumes a fixed left-to-right generation order, which may be sub-optimal. In this work, we propose a novel decoding algorithm— InDIGO—which supports flexible sequence generation in arbitrary orders through insertion operations. We extend Transformer, a state-of-the-art sequence generation model, to efficiently implement the proposed approach, enabling it to be trained with either a pre-defined generation order or adaptive orders obtained from beam-search. Experiments on four real-world tasks, including word order recovery, machine translation, image caption, and code generation, demonstrate that our algorithm can generate sequences following arbitrary orders, while achieving competitive or even better performance compared with the conventional left-to-right generation. The generated sequences show that InDIGO adopts adaptive generation orders based on input information.

scholarly journals Cognification of Program Synthesis—A Systematic Feature-Oriented Analysis and Future Direction

Computers ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 27
Author(s):  
Ahmad F. Subahi
Keyword(s):  
Code Generation ◽  
Program Synthesis ◽  
Domain Analysis ◽  
Automatic Generation ◽  
Feature Model ◽  
Automatic Process ◽  
Generation Model ◽  
Research Database ◽  
Next Generation ◽  
Research Questions

Program synthesis is defined as a software development step aims at achieving an automatic process of code generation that is satisfactory given high-level specifications. There are various program synthesis applications built on Machine Learning (ML) and Natural Language Processing (NLP) based approaches. Recently, there have been remarkable advancements in the Artificial Intelligent (AI) domain. The rise in advanced ML techniques has been remarkable. Deep Learning (DL), for instance, is considered an example of a currently attractive research field that has led to advances in the areas of ML and NLP. With this advancement, there is a need to gain greater benefits from these approaches to cognify synthesis processes for next-generation model-driven engineering (MDE) framework. In this work, a systematic domain analysis is conducted to explore the extent to the automatic generation of code can be enabled via the next generation of cognified MDE frameworks that support recent DL and NLP techniques. After identifying critical features that might be considered when distinguishing synthesis systems, it will be possible to introduce a conceptual design for the future involving program synthesis/MDE frameworks. By searching different research database sources, 182 articles related to program synthesis approaches and their applications were identified. After defining research questions, structuring the domain analysis, and applying inclusion and exclusion criteria on the classification scheme, 170 out of 182 articles were considered in a three-phase systematic analysis, guided by some research questions. The analysis is introduced as a key contribution. The results are documented using feature diagrams as a comprehensive feature model of program synthesis showing alternative techniques and architectures. The achieved outcomes serve as motivation for introducing a conceptual architectural design of the next generation of cognified MDE frameworks.

A Semantic Approach to Deploying Product-Service Systems

2017 ◽  
Vol 8 (1) ◽  
pp. 24-42
Author(s):  
Robert Andrei Buchmann ◽  
Dimitris Karagiannis
Keyword(s):  
Code Generation ◽  
Modeling Method ◽  
Service Systems ◽  
Modeling Languages ◽  
Levels Of Abstraction ◽  
Semantic Approach ◽  
Design Time ◽  
Product Service ◽  
Run Time ◽  
Product Service Systems

Conceptual modeling is commonly employed for two classes of goals: (1) as input for run-time functionality (e.g., code generation) and (2) as support for design-time analysis (e.g., in business process management). An inherent trade-off manifests between such goals, as different levels of abstraction and semantic detail is needed. This has led to a multitude of modeling languages that are conceptually redundant (i.e., they share significant parts of their metamodels) and a dilemma of selecting the most adequate language for each goal. This article advocates the substitution of the selection dilemma with an approach where the modeling method is agilely tailored for the semantic variability required to cover both run-time and design-time concerns. The semantic space enabled by such a method is exposed to model-driven systems as RDF knowledge graphs, whereas the method evolution is managed with the Agile Modeling Method Engineering framework. The argument is grounded in the application area of Product-Service Systems, illustrated by a project-based modeling method.

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