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Proposal of an Experience Framework. for Agile Methodologies. The rationale. Agile methodologies need validations Scarce literature on Experience Framework (for AMs) Previous few experiences have been troubles to access/manage results -Visek. Experience Framework.
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Proposal of an Experience Framework for Agile Methodologies
The rationale • Agile methodologies need validations • Scarce literature on Experience Framework (for AMs) • Previous few experiences have been troubles to access/manage results -Visek
Experience Framework • A structure in which share experience, identify common standards for experiment actuation, collecting data and analyzing data, identify metrics and new approaches • In order to: validate practices, suggest suitable adoptions, increase knowledge in AMs
ExperienceFramework • Create EF for the Agile Methodologies. Some features: • Allowing experiment replications. Replication is fundamental for the consolidation and validation of best practices in every applied discipline. Replication needs a standardization of the experimental process in order to avoid bias • Agile. To be compatible with AMs: repositories continuous integration, experiment replications, easy access, collective information ownership. The consolidated results should be promptly generalized and readily accessible
Examples • Statistics automated tools - SPSS • Patterns and makes predictions from an orderly display of data using concepts of probability and statistics • Statistical methods to make inferences and valid arguments about real-world situations
1. Experiment context and profile • Formative Research A formative research action on the environment may help in defining the experiment standards and detect the pre-test scenario in the company • Information about the industrial circumstances • Background information about the experiment • Information about related research
3. Experiment design • Theoretical Foundation of the Intervention - experiments unit, … • Distribution of Materials by Community Network Members (before the experiment)
4. Standards for the experiment data collection • Data collection instrument • Data collection schedule • Data collection Start Up • Building a measurement support system