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Software Language Engineering

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

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Addressing Cognitive and Social Challenges in Designing and Using Ontologies in the Biomedical Domain
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    Chapter 2 Object Grammars
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    Chapter 3 Profile-Based Abstraction and Analysis of Attribute Grammar Evaluation
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    Chapter 4 Termination Analysis for Higher-Order Attribute Grammars
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    Chapter 5 Metamodelling for Grammarware Researchers
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    Chapter 6 Temporal Constraint Support for OCL
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    Chapter 7 The Program Is the Model: Enabling Transformations@run.time
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    Chapter 8 A Framework for Bidirectional Model-to-Platform Transformations
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    Chapter 9 Model Transformation Co-evolution: A Semi-automatic Approach
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    Chapter 10 Software Language Engineering
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    Chapter 11 Bridging the Chasm between Executable Metamodeling and Models of Computation
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    Chapter 12 Grammatical Inference in Software Engineering: An Overview of the State of the Art
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    Chapter 13 Island Grammar-Based Parsing Using GLL and Tom
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    Chapter 14 Layout-Sensitive Generalized Parsing
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    Chapter 15 PAPAGENO: A Parallel Parser Generator for Operator Precedence Grammars
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    Chapter 16 TouchRAM: A Multitouch-Enabled Tool for Aspect-Oriented Software Design
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    Chapter 17 A Common Foundational Theory for Bridging Two Levels in Ontology-Driven Conceptual Modeling
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    Chapter 18 Declarative Name Binding and Scope Rules
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    Chapter 19 On the Reusable Specification of Non-functional Properties in DSLs
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    Chapter 20 Modular Well-Definedness Analysis for Attribute Grammars
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    Chapter 21 Meta-language Support for Type-Safe Access to External Resources
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    Chapter 22 Approaches and Tools for Implementing Type Systems in Xtext
Overall attention for this book and its chapters
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

Mentioned by

3 tweeters

Readers on

88 Mendeley
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Software Language Engineering
Published by
Lecture notes in computer science, January 2013
DOI 10.1007/978-3-642-36089-3
978-3-64-236088-6, 978-3-64-236089-3

Czarnecki, Krzysztof, Hedin, Görel


Krzysztof Czarnecki, Görel Hedin

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 4 5%
Spain 2 2%
Brazil 2 2%
North Macedonia 1 1%
Portugal 1 1%
Denmark 1 1%
Switzerland 1 1%
Belgium 1 1%
Ecuador 1 1%
Other 0 0%
Unknown 74 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 30%
Student > Master 18 20%
Researcher 12 14%
Student > Doctoral Student 7 8%
Student > Bachelor 6 7%
Other 19 22%
Readers by discipline Count As %
Computer Science 61 69%
Engineering 9 10%
Unspecified 4 5%
Design 3 3%
Business, Management and Accounting 3 3%
Other 8 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 02 August 2018.
All research outputs
of 13,322,622 outputs
Outputs from Lecture notes in computer science
of 7,367 outputs
Outputs of similar age
of 267,818 outputs
Outputs of similar age from Lecture notes in computer science
of 190 outputs
Altmetric has tracked 13,322,622 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,367 research outputs from this source. They receive a mean Attention Score of 4.4. This one has gotten more attention than average, scoring higher than 55% of its peers.
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 267,818 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 190 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 63% of its contemporaries.