Published in ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW 2023), 2023
In this paper, we investigate the skills and competencies developers need to perform good code reviews.
Recommended citation: Pavlína Wurzel Gonçalves, Gül Çalikli, Alexander Serebrenik, Alberto Bacchelli. (2023). "Competencies for Code Review " ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing (CSCW 2023).(2023).
Published in ESEC/FSE 2022 (ACM SIGSOFT Distinguished Paper Award), 2022
In this paper, we investigate the order of files to be reviewed on code review performance.
Recommended citation: Enrico Fregnan, Larissa Braz, Marco D'Ambros, Gül Çalikli, Alberto Bacchelli. (2022). "First Come First Served: The Impact of File Position on Code Review " The ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2022).(2022).
Published in Proceedings of the 44th International Conference on Software Engineering(ICSE 2022), 2022
In this paper, we investigate whether and how explicitly asking developers to focus on security or using checklists during a code review affects the detection of vulnerabilities.
Recommended citation: Larissa Braz, Christian Aeberhard, Gül Çalikli, Alberto Bacchelli. (2022). "Less is more: supporting developers in vulnerability detection during code review. " Proceedings of the 44th International Conference on Software Engineering(ICSE 2022). 1317–1329 (2022).
Published in Proceedings of the 25th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW), 2022
In this paper, we investigate interpersonal conflicts among developers during code review through the thematic analysis of interviews conducted with 22 developers.
Recommended citation: Pavlína Wurzel Gonçalves, Gül Çalikli, Alberto Bacchelli. (2022). "Interpersonal Conflicts During Code Review: Developers' Experience and Practices." Proceedings of the 25th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW). 1-33. https://dl.acm.org/doi/10.1145/3512945
Published in Proceedings of the 43rd International Conference on Software Engineering (ICSE) ( ACM SIGSOFT Distinguished Paper Award ), 2021
In this paper, we study to what extent developers can detect Improper Input Validation (IIV) and investigate underlying reason.
Recommended citation: Larissa Braz, Enrico Fregran, Gül Çalikli, Alberto Bacchelli. (2021). "Why don't Developers Detect Improper Input Validation? ; DROP TABLE Papers ; --." Proceedings of the 43rd International Conference on Software Engineering (ICSE). 499-511.
Published in Proceedings of the 42nd International Conference on Software Engineering (ICSE)( ACM SIGSOFT Distinguished Artifact Award ), 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.
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.
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.
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.
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).
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).
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).