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| Content Provider | ACM Digital Library |
|---|---|
| Author | Ognawala, Saahil Fernández, Daniel Méndez Wagner, Stefan Vetrò, Antonio |
| 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Gathering empirical knowledge is a time consuming task and the results from empirical studies often are soon outdated by new technological solutions.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 summarize 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. |
| Starting Page | 583 |
| Ending Page | 586 |
| Page Count | 4 |
| File Format | |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-05-16 |
| Access Restriction | Subscribed |
| Subject Keyword | Empirical methods Data mining Knowledge transfer Research methods |
| Content Type | Text |
| Resource Type | Article |
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