scholarly journals Domain-Specific Software Engineering Design for Diabetes Mellitus Study Through Gene and Retinopathy Analysis

10.5772/20555 ◽  
2012 ◽  
Author(s):  
Hua Cao ◽  
Deyin Lu ◽  
Bahram Khoobehi
Keyword(s):  
Diabetes Mellitus ◽  
Software Engineering ◽  
Engineering Design ◽  
Domain Specific

Programmatic modeling for biological systems

2021 ◽  
Author(s):  
Alexander L.R. Lubbock ◽  
Carlos F. Lopez
Keyword(s):  
Software Engineering ◽  
Best Practices ◽  
Computational Modeling ◽  
User Interfaces ◽  
Model Development ◽  
List Type ◽  
Cellular Processes ◽  
Domain Specific ◽  
Community Benefits ◽  
Engineering Best Practices

AbstractComputational modeling has become an established technique to encode mathematical representations of cellular processes and gain mechanistic insights that drive testable predictions. These models are often constructed using graphical user interfaces or domain-specific languages, with SBML used for interchange. Models are typically simulated, calibrated, and analyzed either within a single application, or using import and export from various tools. Here, we describe a programmatic modeling paradigm, in which modeling is augmented with best practices from software engineering. We focus on Python - a popular, user-friendly programming language with a large scientific package ecosystem. Models themselves can be encoded as programs, adding benefits such as modularity, testing, and automated documentation generators while still being exportable to SBML. Automated version control and testing ensures models and their modules have expected properties and behavior. Programmatic modeling is a key technology to enable collaborative model development and enhance dissemination, transparency, and reproducibility.HighlightsProgrammatic modeling combines computational modeling with software engineering best practices.An executable model enables users to leverage all available resources from the language.Community benefits include improved collaboration, reusability, and reproducibility.Python has multiple modeling frameworks with a broad, active scientific ecosystem.

scholarly journals Technology transfer: software engineering and engineering design

1992 ◽  
Vol 3 (6) ◽  
pp. 259
Author(s):  
A. Finkelstein ◽  
B. Nuseibeh ◽  
L. Finkelstein ◽  
J. Huang
Keyword(s):  
Technology Transfer ◽  
Software Engineering ◽  
Engineering Design

Quantifying the Impact of Domain Knowledge and Problem Framing on Sequential Decisions in Engineering Design

2018 ◽  
Vol 140 (10) ◽  
Author(s):  
Murtuza Shergadwala ◽  
Ilias Bilionis ◽  
Karthik N. Kannan ◽  
Jitesh H. Panchal
Keyword(s):  
Engineering Design ◽  
Information Acquisition ◽  
Design Problem ◽  
Domain Knowledge ◽  
Optimization Problem ◽  
Domain Specific ◽  
Problem Framing ◽  
Design Solutions ◽  
The Impact ◽  
Domain Independent

Many decisions within engineering systems design are typically made by humans. These decisions significantly affect the design outcomes and the resources used within design processes. While decision theory is increasingly being used from a normative standpoint to develop computational methods for engineering design, there is still a significant gap in our understanding of how humans make decisions within the design process. Particularly, there is lack of knowledge about how an individual's domain knowledge and framing of the design problem affect information acquisition decisions. To address this gap, the objective of this paper is to quantify the impact of a designer's domain knowledge and problem framing on their information acquisition decisions and the corresponding design outcomes. The objective is achieved by (i) developing a descriptive model of information acquisition decisions, based on an optimal one-step look ahead sequential strategy, utilizing expected improvement maximization, and (ii) using the model in conjunction with a controlled behavioral experiment. The domain knowledge of an individual is measured in the experiment using a concept inventory, whereas the problem framing is controlled as a treatment variable in the experiment. A design optimization problem is framed in two different ways: a domain-specific track design problem and a domain-independent function optimization problem (FOP). The results indicate that when the problem is framed as a domain-specific design task, the design solutions are better and individuals have a better state of knowledge about the problem, as compared to the domain-independent task. The design solutions are found to be better when individuals have a higher knowledge of the domain and they follow the modeled strategy closely.

Assessing and Evolving a Domain Specific Language for Formalizing Software Engineering Experiments: An Empirical Study

2014 ◽  
Vol 24 (10) ◽  
pp. 1509-1531 ◽  
Author(s):  
Marília Freire ◽  
Uirá Kulesza ◽  
Eduardo Aranha ◽  
Gustavo Nery ◽  
Daniel Costa ◽  
...  
Keyword(s):  
Software Engineering ◽  
Empirical Study ◽  
Statistical Design ◽  
Domain Specific Language ◽  
Controlled Experiments ◽  
Specific Language ◽  
Model Driven ◽  
Domain Specific ◽  
Computational Support ◽  
Model Driven Approach

The research about the formalization and conduction of controlled experiments in software engineering has reported important insights and guidelines for their organization. However, the computational support to formalize and execute controlled experiments still requires deeper investigation. In this context, this paper presents an empirical study that evaluates a domain-specific language (DSL) proposed to formalize controlled experiments in software engineering. The language is part of a model-driven approach that allows the generation of executable workflows for the experiment participants, according to the statistical design of the experiment. Our study involves the modeling of 16 software engineering experiments to analyze the completeness and expressiveness of the investigated DSL when specifying different controlled experiments. The results highlight several limitations of the DSL that affect the formalization and execution of experiments. These outcomes were used to extend and improve the evaluated DSL. Finally, the improved version of the language was used to model the same experiments in order to illustrate the benefits of the proposed improvements.

Automatic ontology creation using adaptation

2009 ◽  
Vol 24 (1) ◽  
pp. 127-141 ◽  
Author(s):  
Valerie Cross ◽  
Vishal Bathija
Keyword(s):  
Engineering Design ◽  
Language Processing ◽  
Performance Metrics ◽  
General Purpose ◽  
Information Organization ◽  
Domain Specific ◽  
Organization And Management ◽  
Coverage Accuracy ◽  
Ontology Reuse ◽  
Comparable Quality

AbstractOntologies are an emerging means of knowledge representation to improve information organization and management, and they are becoming more prevalent in the domain of engineering design. The task of creating new ontologies manually is not only tedious and cumbersome but also time consuming and expensive. Research aimed at addressing these problems in creating ontologies has investigated methods of automating ontology reuse mainly by extracting smaller application ontologies from larger, more general purpose ontologies. Motivated by the wide variety of existing learning algorithms, this paper describes a new approach focused on the reuse of domain-specific ontologies. The approach integrates existing software tools for natural language processing with new algorithms for pruning concepts not relevant to the new domain and extending the pruned ontology by adding relevant concepts. The approach is assessed experimentally by automatically adapting a design rationale ontology for the software engineering domain to a new one for the related domain of engineering design. The experiment produced an ontology that exhibits comparable quality to previous attempts to automate ontology creation as measured by standard content performance metrics such as coverage, accuracy, precision, and recall. However, further analysis of the ontology suggests that the automated approach should be augmented with recommendations presented to a domain expert who monitors the pruning and extending processes in order to improve the structure of the ontology.

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