Asli Akalin

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2023-155

May 12, 2023

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-155.pdf

Women have historically been underrepresented in software engineering, due in part to an unwelcoming climate pervaded by the widely-held gender bias that men outperform women at programming. Pair programming is both widely used in industry and has been shown to increase student interest in computer science, particularly among women; however if the gender biases are also present in pair programming, its potential for attracting women to the field could be thwarted. We aim to explore the effects of gender bias in pair programming, specifically, in a distributed remote setting in which students cannot directly observe the gender of their peers. Using deception we study whether the perception of the partner, impacts the behavior during programming, the style of communication or the perceived productivity and technical competency of the partner depending on the perceived gender of their partner. To our knowledge, this is the first study specifically focusing on the impact of gender stereotypes and bias within pairs in pair programming. We observed statistically significant effects with moderate to large sizes in four of the 45 dependent variables within the experimental group, comparing how subjects acted when their partners were represented as a man or a woman. When subjects perceived their partners as women, they deleted more characters in the source code window and they displayed lower frequency of informal utterances, reflections and yes/no questions while communicating, compared to when they perceived their partners as men. When partners perceived their subjects as men, they delete fewer source code characters and communicate using more informal utterances, reflections and yes/no questions. These results must be considered carefully because of the small number of subjects; more replications are needed in order to confirm or refute the results in the same and other computer science student populations.

Advisors: Armando Fox

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BibTeX citation:

@mastersthesis{Akalin:EECS-2023-155,
    Author= {Akalin, Asli},
    Title= {An Investigation of Gender Bias in Pair Programming},
    School= {EECS Department, University of California, Berkeley},
    Year= {2023},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-155.html},
    Number= {UCB/EECS-2023-155},
    Abstract= {Women have historically been underrepresented in software engineering, due in part to an unwelcoming climate pervaded by the widely-held gender bias that men outperform women at programming. Pair programming is both widely used in industry and has been shown to increase student interest in computer science, particularly among women; however if the gender biases are also present in pair programming, its potential for attracting women to the field could be thwarted. We aim to explore the effects of gender bias in pair programming, specifically, in a distributed remote setting in which students cannot directly observe the gender of their peers. Using deception we study whether the perception of the partner, impacts the behavior during programming, the style of communication or the perceived productivity and technical competency of the partner depending on the perceived gender of their partner. To our knowledge, this is the first study specifically focusing on the impact of gender stereotypes and bias within pairs in pair programming.
We observed statistically significant effects with moderate to large sizes in four of the 45 dependent variables within the experimental group, comparing how subjects acted when their partners were represented as a man or a woman. When subjects perceived their partners as women, they deleted more characters in the source code window and they displayed lower frequency of informal utterances, reflections and yes/no questions while communicating, compared to when they perceived their partners as men. When partners perceived their subjects as men, they delete fewer source code characters and communicate using more informal utterances, reflections and yes/no questions. These results must be considered carefully because of the small number of subjects; more replications are needed in order to confirm or refute the results in the same and other computer science student populations.},
}

EndNote citation:

%0 Thesis
%A Akalin, Asli 
%T An Investigation of Gender Bias in Pair Programming
%I EECS Department, University of California, Berkeley
%D 2023
%8 May 12
%@ UCB/EECS-2023-155
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2023/EECS-2023-155.html
%F Akalin:EECS-2023-155