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(This book is available at a reduced price for course adoption when ordering six copies or more. Please contact ulysses.guasch@springer-sbm.com for more information.)The purpose of Experimentation in Software Engineering: An Introduction is to introduce students, teachers, researchers, and practitioners to experimentation and experimental evaluation with a focus on software engineering. The objective is, in particular, to provide guidelines for performing experiments evaluating methods, techniques and tools in software engineering. The introduction is provided through a process perspective. The focus is on the steps that we go through to perform experiments and quasi-experiments. The process also includes other types of empirical studies.The motivation for the book emerged from the need for support we experienced when turning our software engineering research more experimental. Several books are available which either treat the subject in very general terms or focus on some specific part of experimentation; most focus on the statistical methods in experimentation. These are important, but there were few books elaborating on experimentation from a process perspective, none addressing experimentation in software engineering in particular.The scope of Experimentation in Software Engineering: An Introduction is primarily experiments in software engineering as a means for evaluating methods, techniques and tools. The book provides some information regarding empirical studies in general, including both case studies and surveys. The intention is to provide a brief understanding of these strategies and in particular to relate them to experimentation.Experimentation in Software Engineering: An Introduction is suitable for use as a textbook or a secondary text for graduate courses, and for researchers and practitioners interested in an empirical approach to software engineering.

Foreword

xi

Preface

xiii

Acknowledgment

xix

Introduction

1

(6)

Software engineering context

2

(2)

Science and software engineering

4

(3)

Empirical strategies

7

(18)

Overview of empirical strategies

8

(2)

Surveys

10

(2)

Survey characteristics

10

(1)

Survey purposes

11

(1)

Data collection

11

(1)

Case studies

12

(2)

Case study arrangements

13

(1)

Confounding factors and other aspects

13

(1)

Experiments

14

(2)

Characteristics

15

(1)

Experiment process

15

(1)

Empirical strategies comparison

16

(1)

Empiricism in a software engineering context

17

(8)

Empirical evaluation of process changes

18

(2)

Quality Improvement Paradigm

20

(1)

Experience Factory

21

(2)

Goal/Question/Metric paradigm

23

(2)

Measurement

25

(6)

Basic concepts

26

(3)

Scale types

27

(1)

Objective and subjective measures

28

(1)

Direct or indirect measures

28

(1)

Measurements in software engineering

29

(2)

Experiment process

31

(10)

Variables, treatments, objects and subjects

33

(2)

Process

35

(6)

Definition

41

(6)

Define experiment

42

(2)

Goal definition template

42

(2)

Example

44

(2)

Summary

46

(1)

Planning

47

(28)

Context selection

48

(1)

Hypothesis formulation

49

(2)

Hypothesis statement

49

(2)

Variables selection

51

(1)

Choice of independent variables

51

(1)

Choice of dependent variables

51

(1)

Selection of subjects

51

(1)

Experiment design

52

(10)

Choice of experiment design

53

(1)

General design principles

53

(1)

Standard design types

54

(8)

Instrumentation

62

(1)

Validity evaluation

63

(3)

Detailed description of validity threats

66

(7)

Conclusion validity

67

(1)

Internal validity

68

(3)

Construct validity

71

(1)

External validity

72

(1)

Priority among types of validity threats

73

(2)

Operation

75

(6)

Preparation

76

(2)

Commit participants

76

(2)

Instrumentation concerns

78

(1)

Execution

78

(1)

Data collection

79

(1)

Experimental environment

79

(1)

Data validation

79

(2)

Analysis and interpretation

81

(34)

Descriptive statistics

82

(8)

Measures of central tendency

83

(1)

Measures of dispersion

84

(2)

Measures of dependency

86

(2)

Graphical visualization

88

(2)

Data set reduction

90

(2)

Hypothesis testing

92

(23)

Basic concept

92

(3)

Parametric and non-parametric tests

95

(1)

Overview of tests

96

(3)

t-test

99

(1)

Mann-Whitney

100

(1)

F-test

101

(1)

Paired t-test

101

(2)

Wilcoxon

103

(1)

Sign test

104

(1)

ANOVA (ANalysis Of VAriance)

105

(2)

Kruskal-Wallis

107

(1)

Chi-2

107

(5)

Model adequacy checking

112

(1)

Drawing conclusions

112

(3)

Presentation and package

115

(4)

An experiment report outline

116

(3)

Introduction

116

(1)

Problem statement

116

(1)

Experiment planning

116

(1)

Experiment operation

117

(1)

Data analysis

117

(1)

Interpretation of results

117

(1)

Discussion and conclusions

118

(1)

Appendix

118

(1)

Literature survey

119

(8)

Inspection experiments

120

(4)

Requirements inspection experiments

120

(3)

Code inspection experiments

123

(1)

Other experiments in Software Engineering

124

(2)

Resources

126

(1)

Example: Experiment process

127

(16)

Definition

128

(1)

Goal definition

128

(1)

Summary of definition

129

(1)

Planning

129

(5)

Context selection

129

(1)

Hypothesis formulation

130

(1)

Variables selection

131

(1)

Selection of subjects

131

(1)

Experiment design

132

(1)

Instrumentation

133

(1)

Validity evaluation

133

(1)

Operation

134

(1)

Preparation

134

(1)

Execution

134

(1)

Data validation

135

(1)

Analysis and interpretation

135

(5)

Descriptive statistics

135

(3)

Data reduction

138

(1)

Hypothesis testing

139

(1)

Summary and conclusions

140

(3)

Example: C versus C++

143

(18)

Introduction and problem statement

143

(2)

Experiment planning

145

(7)

The study environment, PSP

145

(1)

Variables in the study

146

(2)

Analysis

148

(1)

Data used in the study

149

(1)

Validity of results

150

(2)

Analysis and interpretation

152

(6)

Factor analysis

152

(2)

Main analysis

154

(2)

Non-parametric analysis

156

(1)

Learning effects and different knowledge

157

(1)

Conclusions and further work

158

(3)

Exercises

161

(22)

Understanding

162

(2)

Introduction

162

(1)

Empirical strategies

162

(1)

Measurement

162

(1)

Experiment process

162

(1)

Definition

163

(1)

Planning

163

(1)

Operation

163

(1)

Analysis and interpretation

163

(1)

Presentation and package

164

(1)

Training

164

(15)

Normally distributed data

164

(1)

Experience

164

(7)

Programming

171

(1)

Design

172

(3)

Inspections

175

(4)

Reviewing

179

(1)

Assignments

180

(3)

Unit test and code reviews

181

(1)

Inspection methods

181

(1)

Requirements notation

182

(1)

Appendix A: Statistical tables

183

(6)

Appendix B: Experiment process overview

189

(2)

References

191

(8)

About the authors

199

(2)

Index

201

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