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.
Rezensionen / Stimmen
`If you are a researcher, you should master the approaches to empirical software engineering described by Juristo and Moreno.....If you are a practitioner, the advice in this book will enable you to read and assess the studies you find in your journals and at your conferences.....if you are an educator, this book will help you to guide your students in understanding that software engineering is far more than simply having a good technology idea and trying it out on a project.'
Shari Lawrence Pfleeger, Systems/Software, Inc.
Auflage
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Research
Illustrationen
Maße
Höhe: 241 mm
Breite: 160 mm
Dicke: 28 mm
Gewicht
ISBN-13
978-0-7923-7990-4 (9780792379904)
DOI
10.1007/978-1-4757-3304-4
Schweitzer Klassifikation
I: Introduction to Experimentation.- 1. Introduction.- 2. Why Experiment? The Role of Experimentation in Scientific and Technological Research.- 3. How to Experiment?.- II: Designing Experiments.- 4. Basic Notions of Experimental Design.- 5. Experimental Design.- III: Analysing the Experimental Data.- 6. Basic Notions of Data Analysis.- 7. Which is the Better of Two Alternatives? Analysis of One-Factor Designs with Two Alternatives.- 8. Which of k Alternatives is the Best? Analysis for One-Factor Designs and k Alternatives.- 9. Experiments with Undesired Variations: Analysis for Block Designs.- 10. Best Alternatives for More than One Variable: Analysis for Factorial Designs.- 11. Experiments with Incomparable Factor Alternatives: Analysis for Nested Designs.- 12. Fewer Experiments: Analysis for Fractional Factorial Designs.- 13. Several Desired and Undesired Variations: Analysis for Factorial Block Designs.- 14. Non-Parametric Analysis Methods.- 15. How Many Times should an Experiment be Replicated?.- IV: Conclusions.- 16. Some Recommendations on Experimenting.- References.- Annexes.- Annex I: Some Software Project Variables.- Annex II: Some Useful Latin Squares and How They are Used to Build Greco-Latin and Hyper-Greco-Latin Squares.- Annex III: Statistical Tables.
