Basics of Software Engineering Experimentation,9780792379904

Basics of Software Engineering Experimentation

by ;
ISBN13: 9780792379904
ISBN10: 079237990X
Format: Hardcover
Pub. Date: 2000-12-01
Publisher(s): Kluwer Academic Pub
List Price: $349.99

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Summary

Basics of Software Engineering Experimentation is a practical guide to experimentation in a field which has long been underpinned by suppositions, assumptions, speculations and beliefs. It demonstrates to software engineers how Experimental Design and Analysis can be used to validate their beliefs and ideas. The book does not assume its readers have an in-depth knowledge of mathematics, specifying the conceptual essence of the techniques to use in the design and analysis of experiments and keeping the mathematical calculations clear and simple. Basics of Software Engineering Experimentation is practically oriented and is specially written for software engineers, all the examples being based on real and fictitious software engineering experiments.

Table of Contents

List of Figures
xi
List of Tables
xiii
Foreword xix
Acknowledgements xxi
PART I: INTRODUCTION TO EXPERIMENTATION
Introduction
Pre-Scientific Status of Software Engineering
3(3)
Why Don't We Experiment In Se?
6(4)
Kinds of Empirical Studies
10(2)
Amplitude of Experimental Studies
12(5)
Goals of This Book
17(1)
Who Does This Book Target?
18(1)
Objectives To Be Achieved By the Reader of This Book
19(1)
Organisation of the Book
20(3)
Why Experiment? The Role of Experimentation In Scientific and Technological Research
Introduction
23(1)
Research and Experimentation
23(3)
The Social Aspect in Software Engineering
26(1)
The Experimentation/Learning Cycle
27(6)
Scientific Method
33(2)
Why Do Experiments Need To Be Replicated?
35(5)
Empirical Knowledge Versus Theoretical Knowledge
40(5)
How To Experiment?
Introduction
45(1)
Searching for Relationships Among Variables
45(2)
Strategy of Stepwise Refinement
47(2)
Phases of Experimentation
49(2)
Role of Statistics In Experimentation
51(6)
PART II: DESIGNING EXPERIMENTS
Basic Notions of Experimental Design
Introduction
57(1)
Experimental Design Terminology
57(8)
The Software Project As An Experiment
65(5)
Response Variables In Se Experimentation
70(10)
Suggested Exercises
80(3)
Experimental Design
Introduction
83(1)
Experimental Design
83(2)
One-Factor Designs
85(5)
How To Avoid Variations of No Interest To The Experiment: Block Designs
90(7)
Experiments With Multiple Sources of Desired Variation: Factorial Designs
97(5)
What To Do When Factorial Alternatives Are Not Comparable: Nested Designs
102(1)
How To Reduce The Amount of Experiments: Fractional Designs
103(1)
Experiments With Several Desired and Undesired Variations: Factorial Block Designs
104(9)
Importance of Experimental Design and Steps
113(3)
Specific Considerations for Experimental Designs In Software Engineering
116(3)
Suggested Exercises
119(6)
PART III: ANALYSING THE EXPERIMENTAL DATA
Basic Notions of Data Analysis
Introduction
125(1)
Experimental Results As A Sample of A Population
126(2)
Statistical Hypotheses and Decision Making
128(4)
Data Analysis for Large Samples
132(5)
Data Analysis for Small Samples
137(10)
Readers' Guide To Part III
147(4)
Suggested Exercises
151(2)
Which Is The Better of Two Alternatives? Analysis of One-Factor Designs With Two Alternatives
Introduction
153(1)
Statistical Significance of the Difference Between Two Alternatives Using Historical Data
153(7)
Significance of the Difference Between Two Alternatives When No Historical Data Are Available
160(3)
Analysis for Paired Comparison Designs
163(2)
One-Factor Analysis With Two Alternatives In Real Se Experiments
165(8)
Suggested Exercises
173(2)
Which of K Alternatives Is the Best? Analysis for One-Factor Designs and K Alternatives
Introduction
175(1)
Identification of the Mathematical model
176(3)
Validation of the Basic Model That Relates the Experimental Variables
179(7)
Calculations the Factor- and Error-induced Variation in the Response Variable
186(3)
Calculating the Statistical Significance of the Factor-Induced Variation
189(6)
Recommendations Or Conclusions of the Analysis
195(4)
Analysis of One Factor With K Alternatives In Real Se Experiments
199(2)
Suggested Exercises
201(2)
Experiments With Undesired Variations: Analysis for Block Designs
Introduction
203(1)
Analysis for Designs With A Single Blocking Variable
203(13)
Analysis for Designs With Two Blocking Variables
216(3)
Analysis for Two Blocking Variable Designs and Replication
219(1)
Analysis for Designs With More Than Two Blocking Variables
220(7)
Analysis When There Are Missing Data In Block Designs
227(2)
Analysis for Incomplete Block Designs
229(3)
Suggested Exercises
232(3)
Best Alternatives for More Than One Variable: Analysis for Factorial Designs
Introduction
235(1)
Analysis of General Factorial Designs
236(10)
Analysis for Factorial Designs With Two Alternatives Per Factor
246(23)
Analysis for Factorial Designs Without Replication
269(11)
Handling Unbalanced Data
280(6)
Analysis of Factorial Designs In Real Se Experiments
286(3)
Suggested Exercises
289(4)
Experiments With Incomparable Factor Alternatives: Analysis for Nested Designs
Introduction
293(1)
Identification of the Mathematical Model
294(1)
Validation of the Model
294(1)
Calculation of the Variation In the Response Variable Due To Factors and Error
295(1)
Statistical Significance of the Variation In The Response Variable
296(1)
Suggested Exercises
297(2)
Fewer Experiments: Analysis for Fractional Factorial Designs
Introduction
299(1)
Choosing the Experiments In A 2k-p Fractional Factorial Design
300(5)
Analysis for 2k-p Designs
305(5)
Suggested Exercises
310(3)
Several Desired and Undesired Variations: Analysis for Factorial Block Designs
Introduction
313(1)
Identification of the Mathematical Model
314(2)
Calculation of Response Variable Variability
316(1)
Statistical Significance of the Variation In the Response Variable
317(3)
Analysis of Factorial Block Designs In Real Se Experiments
320(1)
Suggested Exercises
321(2)
Non-Parametric Analysis Methods
Introduction
323(1)
Non-Parametric Methods Applicable To Independent Samples
324(4)
Non-Parametric Methods Applicable To Related Samples
328(2)
Non-Parametric Analysis In Real Se Experiments
330(4)
Suggested Exercises
334(3)
How Many Times Should An Experiment Be Replicated?
Introduction
337(1)
Importance of the Number of Replications In Experimentation
338(1)
The Value of the Means of the Alternatives To Be Used To Reject H0 Is Known
338(3)
The Value of the Difference Between Two Means of the Alternatives To Be Used To Reject H0 Is Known
341(1)
The Percentage Value To Be Exceeded By the Standard Deviation To Be Used To Reject H0 Is Known
342(1)
The Difference Between the Means of the Alternatives To Be Used To Reject H0 Is Known for More Than One Factor
343(2)
Suggested Exercises
345(4)
PART IV: CONCLUSIONS
Some Recommendations On Experimenting
Introduction
349(1)
Precautions To Be Taken Into Account In Se Experiments
349(5)
A Guide To Documenting Experimentation
354(5)
REFERENCES 359
ANNEXES
Some Software Project Variables
367
Some Useful Latin Squares and How They Are Used To Build Greco-Latin and Hyper-Greco-Latin Squares
379
Statistical Tables
385

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