Gül Calikli

Registered user since Tue 7 Jul 2020

Name: Gül Calikli

Bio: I am a senior researcher at the Zurich Empirical Software Engineering Team (ZEST) at the Department of Informatics, University of Zurich (UZH), Switzerland.

My research interests include human aspects in software engineering, empirical software engineering and applications of artificial intelligence on building recommendation systems for software engineering. Regarding human aspects, I am mainly interested in cognitive biases. During my PhD, I focussed on confirmation bias, which a tendency of humans to find evidence supporting their hypotheses rather than those refuting them. Here is a talk I gave at Microsoft Research summarising my work on confirmation bias in 2012.

Previously, I was a Postdoctoral Research Fellow with The Open University, United Kingdom, and a Postdoctoral Research Fellow with the Data Science Laboratory in Ryerson University, Toronto, Canada. I am a member of the IEEE Computer Society and ACM. I am also the recipient Best Paper Award at ESEM2013 (Industry Tack) and Chalmers Area of Advance SEED Funding in 2018 and one of the recipients of ACM SIGSOFT Distinguished Artifact Award at ICSE 2020. Before joining ZEST, I worked as lecturer (biträdande universitetslektor) at Chalmers | University of Gothenburg, Sweden.

Country: Switzerland

Affiliation: University of Zürich

Personal website: https://gulcalikli.github.io/

Twitter: https://twitter.com/GulCalikli

Research interests: Human aspects in software engineering, cognitive science


ASE 2021 Committee Member in Program Committee within the Tool Demonstrations-track
ICSE 2021 Author of Data and Materials for: Why don't Developers Detect Improper Input Validation? '; DROP TABLE Papers; -- within the AE - Artifact Evaluation-track
Author of Why don’t Developers Detect Improper Input Validation?'; DROP TABLE Papers; -- within the Technical Track-track
ICSE 2020 Author of Primers or Reminders? The Effects of Existing Review Comments on Code Review within the Technical Papers-track