Empirical Research in Software Engineering

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A01=Ruchika Malhotra
advanced empirical software research applications
Author_Ruchika Malhotra
Average AUC
Bonferroni Dunn Test
case study methodology
Category=PBT
Category=UB
Category=UMZ
Category=UY
change prediction
Change Prediction Models
Change Proneness
cycle
Data Extraction Forms
defect prediction
development
empirical software engineering
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
ESE
fault
Fault Prediction
Fault Proneness
Friedman Test
learning
life
Literature Review
machine
machine learning techniques
Ml Technique
Multi-objective Particle Swarm Optimization
Nemenyi Test
Null Hypothesis
open
Open Source Software
predictive models
programming error analysis
proneness
quantitative analysis methods
replicated and empirical research
repository data mining
Roc Curve
Software
software experiment design
Software Metrics
Software Quality Attribute
Software Repositories
Software Systems
software tools for empirical studies
source
source code repositories
statistical hypothesis testing
techniques
text mining procedures
threats to validity
Type Ii Error
Version Control
Weka Tool
Wilcoxon Mann Whitney Test

Product details

  • ISBN 9781498719728
  • Weight: 1060g
  • Dimensions: 178 x 254mm
  • Publication Date: 05 Oct 2015
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Empirical research has now become an essential component of software engineering yet software practitioners and researchers often lack an understanding of how the empirical procedures and practices are applied in the field. Empirical Research in Software Engineering: Concepts, Analysis, and Applications shows how to implement empirical research processes, procedures, and practices in software engineering.

Written by a leading researcher in empirical software engineering, the book describes the necessary steps to perform replicated and empirical research. It explains how to plan and design experiments, conduct systematic reviews and case studies, and analyze the results produced by the empirical studies.

The book balances empirical research concepts with exercises, examples, and real-life case studies, making it suitable for a course on empirical software engineering. The author discusses the process of developing predictive models, such as defect prediction and change prediction, on data collected from source code repositories. She also covers the application of machine learning techniques in empirical software engineering, includes guidelines for publishing and reporting results, and presents popular software tools for carrying out empirical studies.

Ruchika Malhotra is an assistant professor in the Department of Software Engineering at Delhi Technological University (formerly Delhi College of Engineering). She was awarded the prestigious UGC Raman Fellowship for pursuing post-doctoral research in the Department of Computer and Information Science at Indiana University–Purdue University. She received her master’s and doctorate degrees in software engineering from the University School of Information Technology of Guru Gobind Singh Indraprastha University. She received the IBM Best Faculty Award in 2013 and has published more than 100 research papers in international journals and conferences. Her research interests include software testing, improving software quality, statistical and adaptive prediction models, software metrics, neural nets modeling, and the definition and validation of software metrics.

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