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Software Language Engineering

Overview of attention for book
Cover of 'Software Language Engineering'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 A Language for Software Variation Research
  3. Altmetric Badge
    Chapter 2 Automated Selective Caching for Reference Attribute Grammars
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    Chapter 3 Reference Attribute Grammars for Metamodel Semantics
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    Chapter 4 Modelling GLL Parser Implementations
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    Chapter 5 Metamodel Usage Analysis for Identifying Metamodel Improvements
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    Chapter 6 Domain-Specific Modelling Languages with Algebraic Graph Transformations on RDF
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    Chapter 7 Feature and Meta-Models in Clafer: Mixed, Specialized, and Coupled
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    Chapter 8 Support for the Evolution of C++ Generic Functions
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    Chapter 9 Automated Co-evolution of GMF Editor Models
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    Chapter 10 An Extensive Catalog of Operators for the Coupled Evolution of Metamodels and Models
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    Chapter 11 JTL: A Bidirectional and Change Propagating Transformation Language
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    Chapter 12 Software Engineering and the Semantic Web: A Match Made in Heaven or in Hell?
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    Chapter 13 A Unified Format for Language Documents
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    Chapter 14 Canonical Method Names for Java
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    Chapter 15 Subjective-C
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    Chapter 16 The Level-Agnostic Modeling Language
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    Chapter 17 Debugging in Domain-Specific Modelling
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    Chapter 18 COPE – A Workbench for the Coupled Evolution of Metamodels and Models
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    Chapter 19 DSLTrans: A Turing Incomplete Transformation Language
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    Chapter 20 Translator Generation Using ART
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    Chapter 21 Empirical Language Analysis in Software Linguistics
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    Chapter 22 Interactive Disambiguation of Meta Programs with Concrete Object Syntax
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    Chapter 23 Evaluating a Textual Feature Modelling Language: Four Industrial Case Studies
  25. Altmetric Badge
    Chapter 24 Extending DMM Behavior Specifications for Visual Execution and Debugging
  26. Altmetric Badge
    Chapter 25 Analysing the Cognitive Effectiveness of the BPMN 2.0 Visual Notation
  27. Altmetric Badge
    Chapter 26 Featherweight TeX and Parser Correctness
Attention for Chapter 9: Automated Co-evolution of GMF Editor Models
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

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Chapter title
Automated Co-evolution of GMF Editor Models
Chapter number 9
Book title
Software Language Engineering
Published in
Lecture notes in computer science, January 2011
DOI 10.1007/978-3-642-19440-5_9
Book ISBNs
978-3-64-219439-9, 978-3-64-219440-5
Authors

Davide Di Ruscio, Ralf Lämmel, Alfonso Pierantonio, Di Ruscio, Davide, Lämmel, Ralf, Pierantonio, Alfonso

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 6%
Germany 1 3%
Unknown 31 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 38%
Student > Master 6 18%
Student > Doctoral Student 4 12%
Researcher 3 9%
Student > Bachelor 3 9%
Other 5 15%
Readers by discipline Count As %
Computer Science 28 82%
Engineering 5 15%
Agricultural and Biological Sciences 1 3%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 April 2012.
All research outputs
#14,143,926
of 22,664,644 outputs
Outputs from Lecture notes in computer science
#4,304
of 8,123 outputs
Outputs of similar age
#136,257
of 180,280 outputs
Outputs of similar age from Lecture notes in computer science
#137
of 318 outputs
Altmetric has tracked 22,664,644 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,123 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 180,280 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 318 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.