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Diversity's Double-Edged Sword: Analyzing Race's Effect on Remote Pair Programming Interactions

Shandler A. Mason, Sandeep Kaur Kuttal

TL;DR

The authors' findings revealed elevated productivity scores for mixed-race pairs, with no differences in code quality between same- and mixed-race pairs, and underscores the need for diverse tools and methods to address racial disparities for collaboration.

Abstract

Remote pair programming is widely used in software development, but no research has examined how race affects these interactions. We embarked on this study due to the historical under representation of Black developers in the tech industry, with White developers comprising the majority. Our study involved 24 experienced developers, forming 12 gender-balanced same- and mixed-race pairs. Pairs collaborated on a programming task using the think-aloud method, followed by individual retrospective interviews. Our findings revealed elevated productivity scores for mixed-race pairs, with no differences in code quality between same- and mixed-race pairs. Mixed-race pairs excelled in task distribution, shared decision-making, and role-exchange but encountered communication challenges, discomfort, and anxiety, shedding light on the complexity of diversity dynamics. Our study emphasizes race's impact on remote pair programming and underscores the need for diverse tools and methods to address racial disparities for collaboration.

Diversity's Double-Edged Sword: Analyzing Race's Effect on Remote Pair Programming Interactions

TL;DR

The authors' findings revealed elevated productivity scores for mixed-race pairs, with no differences in code quality between same- and mixed-race pairs, and underscores the need for diverse tools and methods to address racial disparities for collaboration.

Abstract

Remote pair programming is widely used in software development, but no research has examined how race affects these interactions. We embarked on this study due to the historical under representation of Black developers in the tech industry, with White developers comprising the majority. Our study involved 24 experienced developers, forming 12 gender-balanced same- and mixed-race pairs. Pairs collaborated on a programming task using the think-aloud method, followed by individual retrospective interviews. Our findings revealed elevated productivity scores for mixed-race pairs, with no differences in code quality between same- and mixed-race pairs. Mixed-race pairs excelled in task distribution, shared decision-making, and role-exchange but encountered communication challenges, discomfort, and anxiety, shedding light on the complexity of diversity dynamics. Our study emphasizes race's impact on remote pair programming and underscores the need for diverse tools and methods to address racial disparities for collaboration.
Paper Structure (45 sections, 5 figures, 6 tables)

This paper contains 45 sections, 5 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Pair Programming
  4. Benefits of pair programming.
  5. Challenges in fostering pair programming
  6. Impact of sociodemographic characteristics in pair programming.
  7. Race in the United States
  8. History of Racial Inequalities in the US
  9. Race Affects on Collaboration and the Workplace
  10. Race in Technology
  11. The Tech Industry Pipeline Problem
  12. Race in HCI
  13. Methodology
  14. Recruitment
  15. Screening
  16. ...and 30 more sections

Figures (5)

  • Figure 1: Participants self-efficacy before and after the task. Red circles represent a decrease, green circles represent an increase, and black circles represent no change in self-efficacy scores.
  • Figure 2: Self-efficacy questionnaire utilizing a 7-point Likert scale consisting of 9 questions administered both before and after the task ramalingam1998developmentcompeau1995computer.
  • Figure 3: Pair programming preferences questionnaire with 1 (Lowest) - 5 (Highest) scale and 19 questions regarding pair programming with test-driven development.
  • Figure 4: Same-race (BB) vs. same-race (WhWh) vs. mixed-race (BWh) pairs total develop and implement frequencies for driver (blue) and navigator (orange).
  • Figure 5: Percentage of time participants spent using each leadership style.