Empirical Research in Software Engineering: Concepts, Analysis, and Applications, 1st Edition (Hardback) book cover

Empirical Research in Software Engineering

Concepts, Analysis, and Applications, 1st Edition

By Ruchika Malhotra

Chapman and Hall/CRC

472 pages | 140 B/W Illus.

Purchasing Options:$ = USD
Hardback: 9781498719728
pub: 2015-10-05
SAVE ~$24.00
eBook (VitalSource) : 9780429183676
pub: 2016-03-09
from $28.98

FREE Standard Shipping!


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.


"In this book, Dr. Malhotra uses her breadth of software engineering experience and expertise to give the reader coverage of many aspects of empirical software engineering. She covers the essential techniques and concepts needed for a researcher to get started on empirical software engineering research, including metrics, experimental design, analysis and statistical techniques, threats to the validity of any research findings, and methods and tools for empirical software engineering research. … The book provides the reader with an introduction and overview of the field and is also backed by references to the literature, allowing the interested reader to follow up on the methods, tools, and concepts described."

—From the Foreword by Mark Harman, University College London

Table of Contents


What Is Empirical Software Engineering?

Overview of Empirical Studies

Types of Empirical Studies

Empirical Study Process

Ethics of Empirical Research

Importance of Empirical Research

Basic Elements of Empirical Research

Some Terminologies

Concluding Remarks

Systematic Literature Reviews

Basic Concepts

Case Study

Planning the Review

Methods for Presenting Results

Conducting the Review

Reporting the Review

SRs in Software Engineering

Software Metrics


Measurement Basics

Measuring Size

Measuring Software Quality

OO Metrics

Dynamic Software Metrics

System Evolution and Evolutionary Metrics

Validation of Metrics

Practical Relevance

Experimental Design

Overview of Experimental Design

Case Study: Fault Prediction Systems

Research Questions

Reviewing the Literature

Research Variables

Terminology Used in Study Types

Hypothesis Formulation

Data Collection

Selection of Data Analysis Methods

Mining Data from Software Repositories

Configuration Management Systems

Importance of Mining Software Repositories

Common Types of Software Repositories

Understanding Systems

Version Control Systems

Bug Tracking Systems

Extracting Data from Software Repositories

Static Source Code Analysis

Software Historical Analysis

Software Engineering Repositories and Open Research Data Sets

Case Study: Defect Collection and Reporting System for Git Repository

Data Analysis and Statistical Testing

Analyzing the Metric Data

Attribute Reduction Methods

Hypothesis Testing

Statistical Testing

Example—Univariate Analysis Results for Fault Prediction System

Model Development and Interpretation

Model Development

Statistical Multiple Regression Techniques

ML Techniques

Concerns in Model Prediction

Performance Measures for Categorical Dependent Variable

Performance Measures for Continuous Dependent Variable


Model Comparison Tests

Interpreting the Results

Example—Comparing ML Techniques for Fault Prediction

Validity Threats

Categories of Threats to Validity

Example—Threats to Validity in Fault Prediction System

Threats and Their Countermeasures

Reporting Results

Reporting and Presenting Results

Guidelines for Masters and Doctoral Students

Research Ethics and Misconduct

Mining Unstructured Data


Steps in Text Mining

Applications of Text Mining in Software Engineering

Example—Automated Severity Assessment of Software Defect Reports

Demonstrating Empirical Procedures



Related Work

Experimental Design

Research Methodology

Analysis Results

Discussion and Interpretation of Results

Validity Evaluation

Conclusions and Future Work


Tools for Analyzing Data






Comparison of Tools




Exercises and Further Reading appear at the end of most chapters.

About the Author

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.

Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Programming / Games
COMPUTERS / Software Development & Engineering / General
COMPUTERS / Computer Engineering
MATHEMATICS / Probability & Statistics / General