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Search-Based Software Engineering

Overview of attention for book
Cover of 'Search-Based Software Engineering'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 On the Effectiveness of Whole Test Suite Generation
  3. Altmetric Badge
    Chapter 2 Detecting Program Execution Phases Using Heuristic Search
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    Chapter 3 On the Use of Machine Learning and Search-Based Software Engineering for Ill-Defined Fitness Function: A Case Study on Software Refactoring
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    Chapter 4 Producing Just Enough Documentation: The Next SAD Version Problem
  6. Altmetric Badge
    Chapter 5 A Multi-model Optimization Framework for the Model Driven Design of Cloud Applications
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    Chapter 6 A Pattern-Driven Mutation Operator for Search-Based Product Line Architecture Design
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    Chapter 7 Mutation-Based Generation of Software Product Line Test Configurations
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    Chapter 8 Multi-objective Genetic Optimization for Noise-Based Testing of Concurrent Software
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    Chapter 9 Bi-objective Genetic Search for Release Planning in Support of Themes
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    Chapter 10 Combining Stochastic Grammars and Genetic Programming for Coverage Testing at the System Level
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    Chapter 11 Search-Based Software Engineering
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    Chapter 12 A Robust Multi-objective Approach for Software Refactoring under Uncertainty
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    Chapter 13 Towards Automated A/B Testing
  15. Altmetric Badge
    Chapter 14 Random-Weighted Search-Based Multi-objective Optimization Revisited
  16. Altmetric Badge
    Chapter 15 A New Learning Mechanism for Resolving Inconsistencies in Using Cooperative Co-evolution Model
  17. Altmetric Badge
    Chapter 16 Improving Heuristics for the Next Release Problem through Landscape Visualization
  18. Altmetric Badge
    Chapter 17 Machine Learning for User Modeling in an Interactive Genetic Algorithm for the Next Release Problem
  19. Altmetric Badge
    Chapter 18 Transaction Profile Estimation of Queueing Network Models for IT Systems Using a Search-Based Technique
  20. Altmetric Badge
    Chapter 19 Less is More: Temporal Fault Predictive Performance over Multiple Hadoop Releases
  21. Altmetric Badge
    Chapter 20 Babel Pidgin: SBSE Can Grow and Graft Entirely New Functionality into a Real World System
  22. Altmetric Badge
    Chapter 21 Search-Based Software Engineering
  23. Altmetric Badge
    Chapter 22 Repairing and Optimizing Hadoop hashCode Implementations
  24. Altmetric Badge
    Chapter 23 Erratum: Repairing and Optimizing Hadoop hashCode Implementations
Attention for Chapter 20: Babel Pidgin: SBSE Can Grow and Graft Entirely New Functionality into a Real World System
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

news
2 news outlets
blogs
1 blog

Readers on

mendeley
30 Mendeley
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Chapter title
Babel Pidgin: SBSE Can Grow and Graft Entirely New Functionality into a Real World System
Chapter number 20
Book title
Search-Based Software Engineering
Published in
Lecture notes in computer science, August 2014
DOI 10.1007/978-3-319-09940-8_20
Book ISBNs
978-3-31-909939-2, 978-3-31-909940-8
Authors

Mark Harman, Yue Jia, William B. Langdon

Editors

Claire Le Goues, Shin Yoo

Timeline

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Unknown 29 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 37%
Researcher 6 20%
Professor > Associate Professor 3 10%
Student > Master 3 10%
Student > Bachelor 1 3%
Other 4 13%
Unknown 2 7%
Readers by discipline Count As %
Computer Science 21 70%
Engineering 3 10%
Biochemistry, Genetics and Molecular Biology 1 3%
Physics and Astronomy 1 3%
Economics, Econometrics and Finance 1 3%
Other 0 0%
Unknown 3 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 24. 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 26 June 2015.
All research outputs
#1,325,219
of 22,815,414 outputs
Outputs from Lecture notes in computer science
#204
of 8,124 outputs
Outputs of similar age
#14,713
of 236,477 outputs
Outputs of similar age from Lecture notes in computer science
#3
of 220 outputs
Altmetric has tracked 22,815,414 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,124 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done particularly well, scoring higher than 97% 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 236,477 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 220 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.