Assignment: Positive Gender Stereotyping
Assignment: Positive Gender Stereotyping
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Negative Gender Stereotyping
DETAIL SPEAKER NOTES
2) Parental Awareness
DETAIL SPEAKER NOTES AND REFERENCE TO BOTH
References
[1] “A Survey on Domain-Specific Languages for Machine Learning in Big Data” 2016 IEEE
International Conference on Software Science, Technology and Engineering (SWSTE).
[2] “OptiML: An Implicitly Parallel Domain-Specific Language for Machine Learning” 28th
International Conference on Machine Learning, Bellevue, WA, USA, 2011
[3] “Domain Specific Languages for Machine Learning” GPCE 2016.
[4] “Towards Model-Driven Engineering for Big Data Analytics – An Exploratory Analysis
of Domain-Specific Languages for Machine Learning” 2014 47th Hawaii International
Conference on System Sciences.
[5] “An overview of big data opportunities, applications and tools. In Intelligent Systems
and Computer” Vision (ISCV), 2015 (pp. 1-6). IEEE
[6] “ DSL Classification”
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- Statement
- Abstract
- Introduction
- Big Data
- Machine Learning
- Domain Specific Language
- DSL Feature Model
- Language Features
- Transformation Feature
- DSL Tool Features
- DSL Process Features
- Languages Surveyed
- OptiML
- ScalOps
- Scala
- PIG LATIN
- Breukervl
- Possibility of survey of other language
- Conclusions
- Reference
Design Guidelines for Domain Specific Languages.pdf
Design Guidelines for Domain Specific Languages
Gabor Karsai Institute for Software Integrated Systems Vanderbilt University
Nashville, USA
Holger Krahn Software Engineering Group
Department of Computer Science
RWTH Aachen, Germany
Claas Pinkernell Software Engineering Group
Department of Computer Science
RWTH Aachen, Germany
Bernhard Rumpe Software Engineering Group
Department of Computer Science
RWTH Aachen, Germany
Martin Schindler Software Engineering Group
Department of Computer Science
RWTH Aachen, Germany
Steven Völkel Software Engineering Group
Department of Computer Science
RWTH Aachen, Germany
ABSTRACT Designing a new domain specific language is as any other complex task sometimes error-prone and usually time con- suming, especially if the language shall be of high-quality and comfortably usable. Existing tool support focuses on the simplification of technical aspects but lacks support for an enforcement of principles for a good language design. In this paper we investigate guidelines that are useful for de- signing domain specific languages, largely based on our ex- perience in developing languages as well as relying on ex- isting guidelines on general purpose (GPLs) and modeling languages. We defined guidelines to support a DSL devel- oper to achieve better quality of the language design and a better acceptance among its users.
1. INTRODUCTION Designing a new language that allows us to model new
technical properties in a simpler and easier way, describe or implement solutions, or to describe the problem resp. re- quirements in a more concise way is one of the core chal- lenges of computer science. The creation of a new language is a time consuming task, needs experience and is thus usu- ally carried out by specialized language engineers. Nowa- days, the need for new languages for various growing do- mains is strongly increasing. Fortunately, also more sophis- ticated tools exist that allow software engineers to define a new language with a reasonable effort. As a result, an in- creasing number of DSLs (Domain Specific Languages) are designed to enhance the productivity of developers within specific domains. However, these languages often fit only to a rather specific domain problem and are neither of the quality that they can be used by many people nor flexible enough to be easily adapted for related domains.
During the last years, we developed the frameworks Mon- tiCore [13] and GME [2] which support the definition of domain specific languages. Using these frameworks we de- signed several DSLs for a variety of domains, e.g., a textual version of UML/P notations [17] and a language based on function nets in the automotive domain [5]. We experienced that the design of a new DSL is a difficult task because dif- ferent people have a varying perception of what a “good” language actually is.
This of course also depends on the taste of the developer respectively the users, but there are a number of generally acceptable guidelines that assist in language development, making it more a systematic, methodological task and less an intellectual ad-hoc challenge. In this paper we summa- rize, categorize, and amend existing guidelines as well as add our new ones assuming that they improve design and usability of future DSLs.
