Basics of Software Engineering ExperimentationSpringer Science & Business Media, 14 mar 2013 - 396 páginas 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. |
Índice
DESIGNING EXPERIMENTS | 20 |
HOW TO EXPERIMENT? | 45 |
2 | 83 |
4 | 90 |
5 | 97 |
7 | 103 |
11 | 116 |
BASIC NOTIONS OF DATA ANALYSIS | 125 |
EXPERIMENTS WITH INCOMPARABLE FACTOR | 292 |
ANALYSIS | 299 |
SEVERAL DESIRED AND UNDESIRED | 313 |
NONPARAMETRIC ANALYSIS METHODS | 323 |
HOW MANY TIMES SHOULD AN EXPERIMENT | 336 |
| 359 | |
SOME SOFTWARE PROJECT VARIABLES 367 | 366 |
SOME USEFUL LATIN SQUARES AND HOW THEY | 379 |
WHICH IS THE BETTER OF TWO ALTERNATIVES? | 153 |
WHICH OF K ALTERNATIVES IS THE BEST? | 175 |
BEST ALTERNATIVES FOR MORE THAN | 235 |
STATISTICAL TABLES | 385 |
Otras ediciones - Ver todo
Basics of Software Engineering Experimentation Natalia Juristo,Ana M. Moreno No hay ninguna vista previa disponible - 2014 |
Basics of Software Engineering Experimentation Natalia Juristo,Ana M. Moreno No hay ninguna vista previa disponible - 2010 |
Basics of Software Engineering Experimentation Natalia Juristo,Ana M. Moreno No hay ninguna vista previa disponible - 2001 |
Términos y frases comunes
analysis of variance applied Basili block design blocking variable calculated characteristics Cleanroom column complexity considered constraints degrees of freedom described determine discipline discussed in Chapter distribution document elementary experiments empirical errors detected estimation techniques examine example experimental design experimental error experimental unit factor alternatives factorial design flowcharts fractional factorial designs graph of residuals Greco-Latin square groups interaction investigation knowledge Kruskal-Wallis test Latin square mean square measured methods non-parametric non-parametric methods normal distribution null hypothesis number of errors number of replications one-factor parameters performed population procedure productivity programming languages Pseudocode quantitative question randomisation reject relationship response variable running experiments sample scientific shown in Table sign table significant difference similar software development Software Engineering software project software system sort specific SSAB statistically significant Student's t distribution subjects sum of squares Suppose tool unitary experiments valid variation