Data scientists in software engineering seek insight in data collected from software projects to improve software development. The demand for data scientists with domain knowledge in software development is growing rapidly and there is already a shortage of such data scientists.
Data science is a skilled art with a steep learning curve. To shorten that learning curve, this workshop will collect best practices in form of data analysis patterns, that is, analyses of data that leads to meaningful conclusions and can be reused for comparable data. In the workshop we will compile a catalog of such patterns that will help both experienced and emerging data scientists to better communicate about data analysis. The workshop is intended for anyone interested in how to analyze data correctly and efficiently in a community accepted way.
Call for paper
Submission Topics
ACCEPTED PAPERS
Emanuel Giger and Harald Gall. Effect Size Analysis
Rodrigo Souza, Christina Chavez and Roberto Bittencourt. Patterns for Cleaning Up Bug Data
Xiaobing Sun, Ying Chen, Bin Li and Bixin Li. Exploring Software Engineering Data with Formal Co
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