· Replication of empirical studies in different industrial settings
· Issues in sharing datasets
· What happens to empirical results?
· How to choose relevant research results?
· Validation of suggestive investigative questions.
· Communication between researchers and practitioners.
· Stakeholder involvement in empirical studies.
· Establishing relationships based on trust and relevance.
· Dealing with threats in organizational settings.
· Interpreting results in industrial contexts.
· Generalizing the findings from case studies.
· Designing and conducting a family of studies.
· Impact of industrial settings on the design of, and on conducting, case studies, action research, studies in the field, exploratory studies, longitudinal studies, etc. For example: reconciling researchers’ needs for “clean” and complete data and information with practitioners’ situations such as missing data, privacy issues, preservation of reputation, etc.
· Empirical results and their utility in specific industrial contexts (e.g., discovery of multiple components defects and their impact on software maintenance).
· Understanding failures and successes: lessons learned
· Quantitative versus qualitative approaches
· Dealing with perceptions and biases
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