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ECSA 2020
Mon 14 - Fri 18 September 2020 L'Aquila, Italy

The concept of implicit biases is widely seen in many different areas and is regarded as one of the main reasons for the gender disparity between students pursuing degrees in Computer Sciences. Since less than 20% of Computer Science students are female, the information about gender bias in this field is of extreme importance. This research aimed to investigate if and by how much the female students in our department are affected by likely gender bias in their academic life. The data collected in this research was used to evaluate the automatic association that students have towards a specific gender and the computer science field.

Fri 18 Sep

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

14:30 - 15:10
S12: ApplicationsGender Diversity in SA / Research Papers at ECSA 2020 Teams Channel
Chair(s): Matthias Galster University of Canterbury, Elisa Yumi Nakagawa University of São Paulo

Virtualization support: Roberta Capuano

Continuous Experimentation for Automotive Software on the Example of a Heavy Commercial Vehicle in Daily OperationBest paper candidateResearch Track
Research Papers
Federico Giaimo Chalmers University of Technology, Christian Berger University of Gothenburg
Mining Gender Bias: A Preliminary Study on Implicit Biases and Gender Identity in the Department of Computer Science at the Technical University of MunichGender-Diversity
Gender Diversity in SA
Ana Petrovska Technical University of Munich, Germany, Patricia Goldberg Technical University of Munich, Anne Brüggemann-Klein Brüggemann-Klein Technical University of Munich, Anne Nyokabi Siemens AG