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Requirements Engineering: Foundation for Software Quality

Overview of attention for book
Cover of 'Requirements Engineering: Foundation for Software Quality'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Incremental Reconfiguration of Product Specific Use Case Models for Evolving Configuration Decisions
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    Chapter 2 Aligning the Elements of the RUP/UML Business Use-Case Model and the BPMN Business Process Diagram
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    Chapter 3 Modeling and Analyzing Openness Trade-Offs in Software Platforms: A Goal-Oriented Approach
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    Chapter 4 A Contribution Management Framework for Firms Engaged in Open Source Software Ecosystems - A Research Preview
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    Chapter 5 Defect Prevention in Requirements Using Human Error Information: An Empirical Study
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    Chapter 6 Requirements Quality Assurance in Industry: Why, What and How?
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    Chapter 7 The Impact of Specification Structure on Human Memory Performance - Experiences from a First Experiment
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    Chapter 8 How Can You Improve Your As-Is Models? Requirements Analysis Methods Meet GQM
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    Chapter 9 Integrating Goal Model Analysis with Iterative Design
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    Chapter 10 Patterns of Collaboration Driven by Requirements in Agile Software Development Teams
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    Chapter 11 Common Mistakes of Student Analysts in Requirements Elicitation Interviews
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    Chapter 12 How Can Quality Awareness Support Rapid Software Development? – A Research Preview
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    Chapter 13 Using Tags to Support Feature Management Across Issue Tracking Systems and Version Control Systems
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    Chapter 14 From Requirements Monitoring to Diagnosis Support in System of Systems
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    Chapter 15 On the Equivalence Between Graphical and Tabular Representations for Security Risk Assessment
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    Chapter 16 Visualization of Quality of Software Requirements Specification Using Digital Elevation Model
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    Chapter 17 Quality Requirements in Large-Scale Distributed Agile Projects – A Systematic Literature Review
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    Chapter 18 Improving User Story Practice with the Grimm Method: A Multiple Case Study in the Software Industry
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    Chapter 19 Semi-automatic Software Feature-Relevant Information Extraction from Natural Language User Manuals
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    Chapter 20 Mining User Requirements from Application Store Reviews Using Frame Semantics
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    Chapter 21 Using Interaction Data for Continuous Creation of Trace Links Between Source Code and Requirements in Issue Tracking Systems
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    Chapter 22 A Requirements Traceability Approach to Support Mission Assurance and Configurability in the Military
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    Chapter 23 On the Ability of Lightweight Checks to Detect Ambiguity in Requirements Documentation
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    Chapter 24 Using NLP to Detect Requirements Defects: An Industrial Experience in the Railway Domain
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    Chapter 25 Specifying Software Requirements for Safety-Critical Railway Systems: An Experience Report
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    Chapter 26 Usefulness of a Human Error Identification Tool for Requirements Inspection: An Experience Report
Attention for Chapter 5: Defect Prevention in Requirements Using Human Error Information: An Empirical Study
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Mentioned by

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1 tweeter

Citations

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2 Dimensions

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8 Mendeley
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Chapter title
Defect Prevention in Requirements Using Human Error Information: An Empirical Study
Chapter number 5
Book title
Requirements Engineering: Foundation for Software Quality
Published in
Lecture notes in computer science, February 2017
DOI 10.1007/978-3-319-54045-0_5
Book ISBNs
978-3-31-954044-3, 978-3-31-954045-0
Authors

Wenhua Hu, Jeffrey C. Carver, Vaibhav Anu, Gursimran Walia, Gary Bradshaw

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 38%
Student > Doctoral Student 2 25%
Professor > Associate Professor 1 13%
Student > Master 1 13%
Unknown 1 13%
Readers by discipline Count As %
Computer Science 4 50%
Engineering 2 25%
Social Sciences 1 13%
Unknown 1 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 February 2017.
All research outputs
#6,948,046
of 9,116,047 outputs
Outputs from Lecture notes in computer science
#5,107
of 6,824 outputs
Outputs of similar age
#187,295
of 254,660 outputs
Outputs of similar age from Lecture notes in computer science
#35
of 60 outputs
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We're also able to compare this research output to 60 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.