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- W2028509100 abstract "Background/Context: Gathering empirical knowledge is a time consuming task and the results from empirical studies often are soon outdated by new technological solutions. As a result, the impact of empirical results on software engineering practice is often not guaranteed.Objective/Aim: In this paper, we summarise the ongoing discussion on Empirical Software Engineering 2.0 as a way to improve the impact of empirical results on industrial practices. We propose a way to combine data mining and analysis with domain knowledge to enable fast feedback cycles in empirical software engineering research.Method: We identify the key concepts on gathering fast feedback in empirical software engineering by following an experience-based line of reasoning by argument. Based on the identified key concepts, we design and execute a small proof of concept with a company to demonstrate potential benefits of the approach.Results: In our example, we observed that a simple double feedback mechanism notably increased the precision of the data analysis and improved the quality of the knowledge gathered.Conclusion: Our results serve as a basis to foster discussion and collaboration within the research community for a development of the idea." @default.
- W2028509100 created "2016-06-24" @default.
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- W2028509100 date "2015-05-16" @default.
- W2028509100 modified "2023-09-23" @default.
- W2028509100 title "Fast feedback cycles in empirical software engineering research" @default.
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- W2028509100 doi "https://doi.org/10.5555/2819009.2819108" @default.
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