H. Gül Çalikli

H. Gül Çalikli

Senior Researcher in the ZEST Research Group at the UZH Department of Informatics

Selected Publications

Primers or Reminders? The Effects of Existing Review Comments on Code Review

Published in Proceedings of the 42nd International Conference on Software Engineering (ICSE), 2020

In this paper, we explore the robustness of current code review settings in the presence of the availability bias of developers.

Recommended citation: Davide Spadini, Gül Çalikli, Alberto Bacchelli. (2020). "Primers or Reminders? The Effects of Existing Code Review Comments on Code Review." Proceedings of the 42nd International Conference on Software Engineering (ICSE). 1171-1182.

Effects of Explicit Feature Traceability on Program Comprehension.

Published in Proceedings of the 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), 2019

This paper explains the online controlled experiment that is designed and conducted to investigate the effects of explicit feature traceability (e.g., annotations and decompositions) on program comprehension of developers.

Recommended citation: Jacob Krüger, Gül Çalikli, Thorsten Berger, Thomas Leich, Gunter Saake. (2019). "Effects of Explicit Feature Traceability on Program Comprehension." Proceedings of the 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE). 338-349.

Logging you, Logging me: A Replicable Study of Privacy and Sharing Behaviour in Groups of Visual Lifeloggers

Published in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2017

In this paper, we conducted a study in the wild with students on a UK campus to investigate the individual and group based privacy beaviours of visual lifeloggers.

Recommended citation: Blaine A. Price, Avelie Stuart, Gül Çalikli, Ciaran McCormick, Vikram Mehta, Luke Hutton, Arosha Bandara, Mark Levine, Bashar Nuseibeh. (2017). "Logging you, Logging me: A Replicable Study of Privacy and Sharing Behaviour in Groups of Visual Lifeloggers." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). 1(2): 22:1-22:18.

Privacy Dynamics: Learning Privacy Norms for Social Software

Published in Proceedings of The 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, 2016

In this paper, inpsired by social identity theory in socal psychology, we present privacy-aware social software architecture that learns privacy norms for different audience groups based on the sharing behaviours of users.

Recommended citation: Gül Çalikli, Mark Law, Arosha Bandara, Alessandra Russo, Luke Dickens, Blaine Price, Avalie Stuart, Mark Levine, Bashar Nuseibeh. (2017). "Logging you, Logging me: A Replicable Study of Privacy and Sharing Behaviour in Groups of Visual Lifeloggers." Proceedings of The 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. 1(2): 22:1-22:18.

Empirical Analysis of Factors Affecting Confirmation Bias Levels of Software Engineers

Published in Software Quality Journal, 2015

This paper aims to invetigate in the wild the factors affecting confirmation bias levels of software engineers.

Recommended citation: Gül Çalikli, Ayşe Bener. (2015). "Empirical Analysis of Factors Affecting Confirmation Bias Levels of Software Engineers. " Software Quality Journal. 23(4): 695-722 (2015).

Towards a Metric Suite Proposal to Quantify Confirmation Biases of Developers

Published in Proceedings of The 7th International Symposium on Empirical Software Engineering and Measurement (ESEM), 2013

In this paper, In this research, we propose a metric suite that measures confirmation bias of software practitioners. The metrics suite is designed to be used by practitioners during their daily decision making.

Recommended citation: Gül Çalikli, Ayşe Bener, Turgay Aytaç, Övünç Bozcan (2013). "Towards a Metric Suite Proposal to Quantify Confirmation Biases of Developers. " Proceedings of The 7th International Symposium on Empirical Software Engineering and Measurement (ESEM). 363-372 (2013).

Influence of Confirmation Biases of Developers on Software Quality: an Empirical Study

Published in Software Quality Journal, 2013

In this paper, we provide a metric scheme to measure confirmatory behaviour of software developers (i.e., confirmation bias) during their daily practices (e.g., unit testing). In order to assess the effectiveness of the metrics scheme, we perform an empirical study to predict defective parts of software.

Recommended citation: Gül Çalikli, Ayşe Bener. (2013). "Influence of Confirmation Biases of Developers on Software Quality: an Empirical Study. " Software Quality Journal. 21(2): 377-416 (2013).