Table of Links Abstract and 1 Introduction 1.1 The twincode platform 1.2 Related Work 2 Research Questions 3 Variables 3.1 Independent Variables 3.2 Dependent Variables 3.3 Confounding Variables 4 Participants 5 Execution Plan and 5.1 Recruitment 5.2 Training and 5.3 Experiment Execution 5.4 Data Analysis Acknowledgments and References 2 RESEARCH QUESTIONS Our study is based on the hypothesis that gender bias will lead to observable differences based on subjects’ perceptions of the gender of their partners, i.e., they will score men and women differently for similar tasks and also behave differently depending on the perceived gender of their partner. To study our hypothesis, we plan to apply methodological triangulation [7], using several methods to collect data and approaching a complex phenomenon like human behavior from more than one standpoint [5]. In our case, three different data sources will be used: questionnaires completed by the subjects, data collected automatically by the twincode platform, and data produced by two different experimenters analyzing and tagging dialog messages and cheking interrater agreement using Cohen’s kappa coefficient [17]. With respect to the data collected using questionnaires, our research questions are: RQ1 In remote pair programming, does gender bias affect perceived productivity compared to solo programming? RQ2 In remote pair programming, does gender bias affect the partner’s perceived technical competency compared to one’s own technical competency? RQ3 In remote pair programming, does gender bias affect how partners’ skills are perceived? With respect to the data automatically collected by the twincode platform—which could be increased in the future—our research question is: RQ4 In remote pair programming, does gender bias affect the frequencies or relative frequencies with which each partner produces source code additions, source code deletions, successful validations, failed validations, and dialog messages? he manual semantic tagging of the dialog messages classifies each message into two orthogonal dimensions. The first dimension uses the 13 tags proposed in [19] (tags from S to O in Table 2). The second dimension classifies each message as formal or informal. With respect to this data source, our research questions are: RQ5 In remote pair programming, does gender bias affect the frequency or relative frequency of the different types of dialog messages? RQ6 In remote pair programming, does gender bias affect the relative frequency of formal and informal dialog messages? Authors:
(1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es);
(2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es);
(3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es);
(4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu);
(5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu);
(6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu). This paper is available on arxiv under CC BY 4.0 DEED license. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 1.1 The twincode platform 1.1 The twincode platform 1.2 Related Work 1.2 Related Work 2 Research Questions 2 Research Questions 3 Variables 3 Variables 3.1 Independent Variables 3.1 Independent Variables 3.2 Dependent Variables 3.2 Dependent Variables 3.3 Confounding Variables 3.3 Confounding Variables 4 Participants 4 Participants 5 Execution Plan and 5.1 Recruitment 5 Execution Plan and 5.1 Recruitment 5.2 Training and 5.3 Experiment Execution 5.2 Training and 5.3 Experiment Execution 5.4 Data Analysis 5.4 Data Analysis Acknowledgments and References Acknowledgments and References 2 RESEARCH QUESTIONS Our study is based on the hypothesis that gender bias will lead to observable differences based on subjects’ perceptions of the gender of their partners, i.e., they will score men and women differently for similar tasks and also behave differently depending on the perceived gender of their partner. To study our hypothesis, we plan to apply methodological triangulation [7], using several methods to collect data and approaching a complex phenomenon like human behavior from more than one standpoint [5]. In our case, three different data sources will be used: questionnaires completed by the subjects, data collected automatically by the twincode platform, and data produced by two different experimenters analyzing and tagging dialog messages and cheking interrater agreement using Cohen’s kappa coefficient [17]. With respect to the data collected using questionnaires, our research questions are: RQ1 In remote pair programming, does gender bias affect perceived productivity compared to solo programming? RQ2 In remote pair programming, does gender bias affect the partner’s perceived technical competency compared to one’s own technical competency? RQ3 In remote pair programming, does gender bias affect how partners’ skills are perceived? With respect to the data automatically collected by the twincode platform—which could be increased in the future—our research question is: RQ4 In remote pair programming, does gender bias affect the frequencies or relative frequencies with which each partner produces source code additions, source code deletions, successful validations, failed validations, and dialog messages? he manual semantic tagging of the dialog messages classifies each message into two orthogonal dimensions. The first dimension uses the 13 tags proposed in [19] (tags from S to O in Table 2). The second dimension classifies each message as formal or informal. With respect to this data source, our research questions are: RQ5 In remote pair programming, does gender bias affect the frequency or relative frequency of the different types of dialog messages? RQ6 In remote pair programming, does gender bias affect the relative frequency of formal and informal dialog messages? Authors: (1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es); (2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es); (3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es); (4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu); (5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu); (6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu). Authors: Authors: (1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es); (2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es); (3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es); (4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu); (5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu); (6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

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