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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 5.4 Data Analysis During the manual tagging of the dialog messages, all pairs in which the gender of any of the peers is disclosed in any way are excluded from the data analysis. Then, before analyzing response variables, the internal consistency of the questionnaire data is checked using Cronbach’s alpha and Kaiser criterion. After that, for every dependent variable v, we compute the distance between the two in–pair tasks as the absolute value of the difference, i.e. | v(t2) – v(t1) |. Ideally, this distance should be lower for the students in the control group (no information about partners’ genders) than for those in the experimental group (with two different perceived partners’ genders at t1 and t2). Therefore, for every variable, we perform a t–test to detect distance differences between the groups. Then, using the data from the experimental group only, we perform a t–test to detect differences in the scores of every dependent variable between perceived partner’s gender, i.e. to detect differences in the scores when partners are perceived as men vs. as women. Finally, to detect a potential interaction between the perceived partner’s gender and the subject’s gender, we perform a mixed–model ANOVA with the perceived gender as a within– subjects variable and subject’s gender as a between–subjects variable. As complementary analyses, we also study (i) the correlation between the induced and the perceived gender for the subjects in the experimental group, and the distribution of the perceived gender (if any) in the control group; and (ii) the potential cultural impact of the different locations at which the experiment is carried out. All the data analysis will be performed using R scripts, that will be available in a public repository together with the datasets in the corresponding laboratory package. 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 5.4 Data Analysis During the manual tagging of the dialog messages, all pairs in which the gender of any of the peers is disclosed in any way are excluded from the data analysis. Then, before analyzing response variables, the internal consistency of the questionnaire data is checked using Cronbach’s alpha and Kaiser criterion. After that, for every dependent variable v, we compute the distance between the two in–pair tasks as the absolute value of the difference, i.e. | v(t2) – v(t1) |. Ideally, this distance should be lower for the students in the control group (no information about partners’ genders) than for those in the experimental group (with two different perceived partners’ genders at t1 and t2). Therefore, for every variable, we perform a t–test to detect distance differences between the groups. Then, using the data from the experimental group only, we perform a t–test to detect differences in the scores of every dependent variable between perceived partner’s gender, i.e. to detect differences in the scores when partners are perceived as men vs. as women. Finally, to detect a potential interaction between the perceived partner’s gender and the subject’s gender, we perform a mixed–model ANOVA with the perceived gender as a within– subjects variable and subject’s gender as a between–subjects variable. As complementary analyses, we also study (i) the correlation between the induced and the perceived gender for the subjects in the experimental group, and the distribution of the perceived gender (if any) in the control group; and (ii) the potential cultural impact of the different locations at which the experiment is carried out. All the data analysis will be performed using R scripts, that will be available in a public repository together with the datasets in the corresponding laboratory package. 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

How Twincode Uncovers Gender Bias in Remote Pair Programming

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 5 EXECUTION PLAN We plan to perform a baseline experiment at the University of Seville in the first semester of the 2021–2022 academic course, and then, replicate the experiment at the University of California Berkeley at the beginning of the second semester. At each location, the experimental material provided to the students will be localized, i.e., in Spanish in Spain and in English at the USA, and the translation will be carefully checked by bilingual experimenters.[1] Independently of being the baseline or the replication, the execution plan of the twincode study consists of the steps described below. 5.1 Recruitment In this initial step, we plan to motivate the students to voluntarily participate in the study as an interesting experience in remote pair programming but without mentioning that the main goal is to study the effect of gender bias. We also remark that for the purpose of the study, they must remain anonymous to their partners, so they must neither mention nor ask any personal information. The interested students must register in the twincode platform providing some demographic data and accepting the participation conditions. 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. [1] At the time of writing, discussions are underway for another replication at the Northern Technical University at Ecuador. If this replication is eventually carried out, the experimental material will also be adapted to the local variant of Spanish if deemed necessary. 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 5 EXECUTION PLAN We plan to perform a baseline experiment at the University of Seville in the first semester of the 2021–2022 academic course, and then, replicate the experiment at the University of California Berkeley at the beginning of the second semester. At each location, the experimental material provided to the students will be localized, i.e., in Spanish in Spain and in English at the USA, and the translation will be carefully checked by bilingual experimenters.[1] Independently of being the baseline or the replication, the execution plan of the twincode study consists of the steps described below. 5.1 Recruitment In this initial step, we plan to motivate the students to voluntarily participate in the study as an interesting experience in remote pair programming but without mentioning that the main goal is to study the effect of gender bias. We also remark that for the purpose of the study, they must remain anonymous to their partners, so they must neither mention nor ask any personal information. The interested students must register in the twincode platform providing some demographic data and accepting the participation conditions. 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 [1] At the time of writing, discussions are underway for another replication at the Northern Technical University at Ecuador. If this replication is eventually carried out, the experimental material will also be adapted to the local variant of Spanish if deemed necessary.

Twincode Execution Plan and Recruitment

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 1.1 The twincode platform To support our study, we have developed the twincode remote pair programming platform, which manages the registration of students, the random allocation to gender-balanced groups, the random allocation into pairs, the random assignment of programming exercises to pairs, and the automatic collection of interaction metrics and dialog messages. As shown in Figure 1, twincode offers a source code editor where the students concurrently develop the solution to a proposed exercise and can validate it against several test cases. It also offers a chat window, where they can collaborate to solve the exercise. Note that a gendered avatar is displayed for the student in the experimental group only (right), but not for the one in the control group (left). 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 1.1 The twincode platform To support our study, we have developed the twincode remote pair programming platform, which manages the registration of students, the random allocation to gender-balanced groups, the random allocation into pairs, the random assignment of programming exercises to pairs, and the automatic collection of interaction metrics and dialog messages. As shown in Figure 1, twincode offers a source code editor where the students concurrently develop the solution to a proposed exercise and can validate it against several test cases. It also offers a chat window, where they can collaborate to solve the exercise. Note that a gendered avatar is displayed for the student in the experimental group only (right), but not for the one in the control group (left). 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

How Twincode Measures Gender Bias in Remote Pair Programming Environments

Technologically advanced from the moment you land on their website. Slick, eye-catching and “dynamic” due to animation elements that complicate the process of perceiving and evaluating the company's work, leading to a stereotypical assumption that we're dealing with more of a marketing agency than a 3D visualization studio. Simple and minimalist, with portfolios full of works “in line with the deadly dull”. Bright and garish, inevitably catching the eye after the “sterile” teams (who either follow current trends and have the ability to create the illusion of reality, or struggle with their own brand positioning). Subtly attractive, reflecting a true commitment to sustainability through every “clean line” of architectural objects. Old-fashioned, but with a tendency to conquer modernity with their authenticity... I have reviewed dozens of 3D visualization studios scattered across the globe, from Switzerland’s Valaiselena to the bustling streets of London. And I can say that, depending on cultural codes (or perhaps a simple “national” alignment in tastes) or the basic “technical” potential, each of them may not fit “size-wise” for everyone. And this is despite the fact that, at first glance, the team seems to cover every possible need of the client. This initial assessment can often be misleading – turning into disappointment for the "customer" who has already envisioned vibrant yacht renders on their website! But they don’t do those... Due to the technological advancements in the 3D field, its products (ranging from architectural visualizations or panoramic “shots” of houses integrated into mountain landscapes, to spaces designed for the metaverse) have become powerful tools for extracting either short-term or long-term, commercial or mutual benefits. That’s all true. But this "everything" also largely depends on the situation, as the individual request of the client requires a specific and precise solution. What does this mean? It means that browsing and getting inspired by portfolios is pleasant (especially when the renders are detailed, vivid and high quality), but it’s better when their work aligns with the “products” they claim to deliver. It means that the lack of a clearly outlined development process may either indicate an unprofessional approach or, conversely, be an intriguing part of marketing... Who knows? I only know the basic truths that one should pay attention to when choosing that coveted, responsible studio. And here is their list! Influential Rendering – a Business Boost The demand for 3D visualization and rendering studios is driven by the rapid growth of the impact that "beautiful" images have, first and foremost, on business. Through a comprehensive presentation, where the effectiveness of visualizations – such as for hotel chains – is backed by a well-structured narrative of the project’s goals, it is possible not only to make a powerful impression on investors but also to demonstrate the advantages of investing in infrastructure development. The 3D product is integrated into virtually every sphere of life. New beauty salons, existing online stores losing loyal customers, real estate developers needing to generate demand for properties before they are completed, and marketing agencies offering services on behalf of individual clients, all require 3D visualizations. In general, an accurate "image" is a crucial step that influences the perception of the final product, whether it’s an architectural structure, interior design, or a new product on the market. For this reason, it’s essential to choose the right 3D visualization studio that will understand your idea and bring it to life with precision and detail. Versatility and Diversity of the Portfolio Our main concern is creating spaces or objects that touch people's lives. Good design should offer a singular experience to those who encounter or inhabit it; it should be memorable, characterful, and surprising – a timeless design that speaks powerfully to both place and people – said the founder of Autoban. I believe that reflecting designs in a portfolio that remain relevant beyond the constraints of time is something only true professionals can achieve. And if you can assess this quality over time, you are likely dealing with the best in the field. However, what truly sets the company apart in a competitive market is its ability to work effectively not only with interiors but also with exteriors – whether it’s homes, villas, yachts, or cars. This means the portfolio must be diverse. Such a level of "preparation" may subtly hint at hidden technical aspects of the company that the client might not be aware of until they speak with a manager: the number of employees (after all, one person can’t do everything), the ability to meet expectations (what if a client needs a faucet model that is available in their portfolio?), and so on. The portfolio reflects the product’s specificity and signals a green light to the client that the product is ready to be "ordered". How to Adapt Unreal Engine for Archviz Needs This is a question that doesn’t need to be asked by those who keep up with not only the updates from Epic Games but also stay tuned to the latest releases of rendering engines and modeling software in general. In fact, the combination of the words “adaptation” and “Unreal Engine” is increasingly becoming a marker of an artist’s qualifications – specifically, 3D visualization studios. And here, we’re not talking about blindly following a trend that would automatically label you as a programmer who has mastered only the Blueprint system (although this is a bonus in itself). No. What’s important is always knowing how to extract the knowledge that will help optimize and streamline the process of creating projects faster (without sacrificing quality). After all, Twinmotion within the context of archviz is equally invaluable, isn’t it? Understanding Client Needs Asking the right questions is a valuable skill, and it should be inherent to any studio. The success of a project lies in the ability to "extract" details, as this ensures accurate execution and eliminates any risks of errors. The better the studio understands the tasks and goals, the faster – and even more cost-effectively –they can bring your vision to life. \In our field, the ability to accurately understand the context in which a client presents their task and to identify their expectations is key to success. The better we grasp the essence of their idea, the faster and more efficiently we can bring that vision to life through 3D visualization technologies. This enables us to achieve excellent results while also reducing costs for the client – said Alexander Kasperovich, CEO Studio 57. Transparency of the Process I’ve already touched on the topic of “transparency” when discussing how 3D studios fill their websites with detailed information about their working algorithms. This concept extends even further – into communication. The absence of uncertainty is a guarantee that the client will be satisfied, leave a positive review about how carefully we addressed their concerns, and even recommend us to their acquaintances. Flexibility or project adjustments A studio that is open to revisions demonstrates its commitment to quality that borders on perfection (understanding that true perfection isn’t always attainable). A good company knows that top-tier 3D visualization doesn’t happen on the first try and is always ready to make adjustments. Openness to constructive criticism is a crucial aspect of successful collaboration. Technologically advanced from the moment you land on their website. Slick, eye-catching and “dynamic” due to animation elements that complicate the process of perceiving and evaluating the company's work, leading to a stereotypical assumption that we're dealing with more of a marketing agency than a 3D visualization studio. Simple and minimalist, with portfolios full of works “in line with the deadly dull”. Bright and garish, inevitably catching the eye after the “sterile” teams (who either follow current trends and have the ability to create the illusion of reality, or struggle with their own brand positioning). Subtly attractive, reflecting a true commitment to sustainability through every “clean line” of architectural objects. Old-fashioned, but with a tendency to conquer modernity with their authenticity... I have reviewed dozens of 3D visualization studios scattered across the globe, from Switzerland’s Valaiselena to the bustling streets of London. And I can say that, depending on cultural codes (or perhaps a simple “national” alignment in tastes) or the basic “technical” potential, each of them may not fit “size-wise” for everyone. And this is despite the fact that, at first glance, the team seems to cover every possible need of the client. This initial assessment can often be misleading – turning into disappointment for the "customer" who has already envisioned vibrant yacht renders on their website! But they don’t do those... Due to the technological advancements in the 3D field, its products (ranging from architectural visualizations or panoramic “shots” of houses integrated into mountain landscapes, to spaces designed for the metaverse) have become powerful tools for extracting either short-term or long-term, commercial or mutual benefits. That’s all true. But this "everything" also largely depends on the situation, as the individual request of the client requires a specific and precise solution. What does this mean? It means that browsing and getting inspired by portfolios is pleasant (especially when the renders are detailed, vivid and high quality), but it’s better when their work aligns with the “products” they claim to deliver. It means that the lack of a clearly outlined development process may either indicate an unprofessional approach or, conversely, be an intriguing part of marketing... Who knows? I only know the basic truths that one should pay attention to when choosing that coveted, responsible studio. And here is their list! Influential Rendering – a Business Boost Influential Rendering – a Business Boost The demand for 3D visualization and rendering studios is driven by the rapid growth of the impact that "beautiful" images have, first and foremost, on business. Through a comprehensive presentation, where the effectiveness of visualizations – such as for hotel chains – is backed by a well-structured narrative of the project’s goals, it is possible not only to make a powerful impression on investors but also to demonstrate the advantages of investing in infrastructure development. The 3D product is integrated into virtually every sphere of life. New beauty salons, existing online stores losing loyal customers, real estate developers needing to generate demand for properties before they are completed, and marketing agencies offering services on behalf of individual clients, all require 3D visualizations. In general, an accurate "image" is a crucial step that influences the perception of the final product, whether it’s an architectural structure, interior design, or a new product on the market. For this reason, it’s essential to choose the right 3D visualization studio that will understand your idea and bring it to life with precision and detail. Versatility and Diversity of the Portfolio Versatility and Diversity of the Portfolio Our main concern is creating spaces or objects that touch people's lives. Good design should offer a singular experience to those who encounter or inhabit it; it should be memorable, characterful, and surprising – a timeless design that speaks powerfully to both place and people Our main concern is creating spaces or objects that touch people's lives. Good design should offer a singular experience to those who encounter or inhabit it; it should be memorable, characterful, and surprising – a timeless design that speaks powerfully to both place and people – said the founder of Autoban . Autoban Autoban I believe that reflecting designs in a portfolio that remain relevant beyond the constraints of time is something only true professionals can achieve. And if you can assess this quality over time, you are likely dealing with the best in the field. However, what truly sets the company apart in a competitive market is its ability to work effectively not only with interiors but also with exteriors – whether it’s homes, villas, yachts, or cars. This means the portfolio must be diverse. Such a level of "preparation" may subtly hint at hidden technical aspects of the company that the client might not be aware of until they speak with a manager: the number of employees (after all, one person can’t do everything), the ability to meet expectations (what if a client needs a faucet model that is available in their portfolio?), and so on. The portfolio reflects the product’s specificity and signals a green light to the client that the product is ready to be "ordered". How to Adapt Unreal Engine for Archviz Needs How to Adapt Unreal Engine for Archviz Needs This is a question that doesn’t need to be asked by those who keep up with not only the updates from Epic Games but also stay tuned to the latest releases of rendering engines and modeling software in general. Epic Games Epic Games In fact, the combination of the words “adaptation” and “Unreal Engine” is increasingly becoming a marker of an artist’s qualifications – specifically, 3D visualization studios. And here, we’re not talking about blindly following a trend that would automatically label you as a programmer who has mastered only the Blueprint system (although this is a bonus in itself). No. What’s important is always knowing how to extract the knowledge that will help optimize and streamline the process of creating projects faster (without sacrificing quality). After all, Twinmotion within the context of archviz is equally invaluable, isn’t it? Understanding Client Needs Understanding Client Needs Asking the right questions is a valuable skill, and it should be inherent to any studio. The success of a project lies in the ability to "extract" details, as this ensures accurate execution and eliminates any risks of errors. The better the studio understands the tasks and goals, the faster – and even more cost-effectively –they can bring your vision to life. \In our field, the ability to accurately understand the context in which a client presents their task and to identify their expectations is key to success. The better we grasp the essence of their idea, the faster and more efficiently we can bring that vision to life through 3D visualization technologies. This enables us to achieve excellent results while also reducing costs for the client \In our field, the ability to accurately understand the context in which a client presents their task and to identify their expectations is key to success. The better we grasp the essence of their idea, the faster and more efficiently we can bring that vision to life through 3D visualization technologies. This enables us to achieve excellent results while also reducing costs for the client – said Alexander Kasperovich, CEO Studio 57. CEO Studio 57. CEO Studio 57. Transparency of the Process I’ve already touched on the topic of “transparency” when discussing how 3D studios fill their websites with detailed information about their working algorithms. This concept extends even further – into communication. The absence of uncertainty is a guarantee that the client will be satisfied, leave a positive review about how carefully we addressed their concerns, and even recommend us to their acquaintances. Flexibility or project adjustments Flexibility or project adjustments A studio that is open to revisions demonstrates its commitment to quality that borders on perfection (understanding that true perfection isn’t always attainable). A good company knows that top-tier 3D visualization doesn’t happen on the first try and is always ready to make adjustments. Openness to constructive criticism is a crucial aspect of successful collaboration.

100% Match: How to Know You’ve Found a 3D Visualization Studio that Will Bring Your Dreams to Life?

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 ACKNOWLEDGMENTS We would like to thank the students who volunteered to participate in the pilot studies at the University of California, Berkeley and the University of Seville. We particularly acknowledge Vron Vance’s assistance regarding inclusive language around gender identity. This work has been partially supported by FEDER/Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación under projects OPHELIA (RTI2018–101204–B–C22), HORATIO (RTI2018- 101204–B–C21), and Junta de Andalucía under project EKIPMENTPLUS (P18–FR–2895). REFERENCES [1] Ahmad Al-Jarrah and Enrico Pontelli. 2016. On the effectiveness of a collaborative virtual pair-programming environment. In International Conference on Learning and Collaboration Technologies. Springer, 583–595. [2] E. A. Chaparro, Aybala Yuksel, Pablo Romero, and Sallyann Bryant. 2005. Factors Affecting the Perceived Effectiveness of Pair Programming in Higher Education. In Proceedings of the 17th Workshop of the Psychology of Programming Interest Group. [3] Kyungsub S. Choi. 2013. Evaluating Gender Significance within a Pair Programming Context. In Proceedings of the 2013 46th Hawaii International Conference on System Sciences (HICSS ’13). IEEE Computer Society, USA, 4817–4825. https://doi.org/10.1109/HICSS.2013.209 [4] Kyungsub Stephen Choi. 2015. A comparative analysis of different gender pair combinations in pair programming. Behaviour & Information Technology 34, 8 (2015), 825–837. https://doi.org/10.1080/0144929X.2014.937460 arXiv:https://doi.org/10.1080/0144929X.2014.937460 [5] Louis Cohen, Lawrence Manion, and Keith Morrison. 2018. Research Methods in Education (8th ed.). Routledge. [6] Bernardo José da Silva Estácio and Rafael Prikladnicki. 2015. Distributed Pair Programming: A Systematic Literature Review. Information and Software Technology 63 (2015), 1–10. [7] N.K. Denzin. 2006. Sociological Methods: A Sourcebook (5th ed.). Aldine Transaction. [8] Dean Eckles, René F. Kizilcec, and Eytan Bakshy. 2016. Estimating peer effects in networks with peer encouragement designs. Proceedings of the National Academy of Sciences 113, 27 (2016), 7316–7322. https://doi.org/10.1073/pnas.1511201113 arXiv:https://www.pnas.org/content/113/27/7316.full.pdf [9] O. Gómez, M. Solari, C. Calvache, and A. Ledezma-Carrizalez. 2017. A Controlled Experiment on Productivity of Pair Programming Gender Combinations: Preliminary Results. In Proceedings of the XX Ibero–American Conference on Software Engineering. 197–210. [10] Brian Hanks, Sue Fitzgerald, Renée McCauley, Laurie Murphy, and Carol Zander. 2011. Pair programming in education: A literature review. Computer Science Education 21, 2 (2011), 135–173. [11] Jo E. Hannay, Erik Arisholm, Harald Engvik, and Dag I.K. Sjoberg. 2010. Effects of Personality on Pair Programming. IEEE Transactions on Software Engineering 36, 1 (2010), 61–80. https://doi.org/10.1109/TSE.2009.41 [12] Sarah I. Hofer. 2015. Studying Gender Bias in Physics Grading: The role of teaching experience and country. International Journal of Science Education 37, 17 (2015), 2879–2905. https://doi.org/10.1080/09500693.2015.1114190 arXiv:https://doi.org/10.1080/09500693.2015.1114190 [13] Lindsay Jarratt, Nicholas A. Bowman, K.C. Culver, and Alberto Maria Segre. 2019. A Large-Scale Experimental Study of Gender and Pair Composition in Pair Programming. In Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education (Aberdeen, Scotland Uk) (ITiCSE ’19). Association for Computing Machinery, New York, NY, USA, 176–181. https: //doi.org/10.1145/3304221.3319782 [14] N. Katira, L. Williams, and J. Osborne. 2005. Towards increasing the compatibility of student pair programmers. In Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005. 625–626. https://doi.org/10.1109/ICSE. 2005.1553618 [15] K. Kaur Chahal, A. Kaur, and M. Saini. 2021. Research and Evidence in Software Engineering: From Empirical Studies to Open Source Artifacts. Taylor & Francis Group, Chapter Empirical Studies on Using Pair Programming as a Pedagogical Tool in Higher Education Courses: A Systematic Literature Review, 251–287. [16] Sandeep Kaur Kuttal, Kevin Gerstner, and Alexandra Bejarano. 2019. Remote Pair Programming in Online CS Education: Investigating through a Gender Lens. In 2019 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). 75–85. https://doi.org/10.1109/VLHCC.2019.8818790 [17] J.R. Landis and G.G. Koch. 1977. The Measurement of Observer Agreement for Categorical Data. Biometrics 33, 1 (1977), 159–174. [18] Martell, D. M. Lane, and C. Emrich. 1996. Male-female differences: A computer simulation. American Psychologist 2, 51 (7 1996), 157–158. https://doi.org/10. 1037/0003-066X.51.2.157 [19] Fernando J. Rodríguez, Kimberly Michelle Price, and Kristy Elizabeth Boyer. 2017. Exploring the Pair Programming Process: Characteristics of Effective Collaboration. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (Seattle, Washington, USA) (SIGCSE ’17). Association for Computing Machinery, New York, NY, USA, 507–512. https://doi.org/10. 1145/3017680.3017748 [20] Norsaremah Salleh, Emilia Mendes, and John Grundy. 2014. Investigating the effects of personality traits on pair programming in a higher education setting through a family of experiments. Empirical Software Engineering 19, 3 (2014), 714–752. [21] Norsaremah Salleh, Emilia Mendes, John Grundy, and Giles St J Burch. 2010. The effects of neuroticism on pair programming: an empirical study in the higher education context. In Proceedings of the 2010 ACM-IEEE international symposium on empirical software engineering and measurement. 1–10. [22] David Stotts, Laurie Williams, Nachiappan Nagappan, Prashant Baheti, Dennis Jen, and Anne Jackson. 2003. Virtual teaming: Experiments and experiences with distributed pair programming. In Conference on Extreme Programming and Agile Methods. Springer, 129–141. [23] Lynda Thomas, Mark Ratcliffe, and Ann Robertson. 2003. Code Warriors and Code-a-Phobes: A Study in Attitude and Pair Programming. In Proceedings of the 34th SIGCSE Technical Symposium on Computer Science Education (Reno, Navada, USA) (SIGCSE ’03). Association for Computing Machinery, New York, NY, USA, 363–367. https://doi.org/10.1145/611892.612007 [24] Linda L. Werner, Brian Hanks, and Charlie McDowell. 2004. Pair-Programming Helps Female Computer Science Students. J. Educ. Resour. Comput. 4, 1 (March 2004), 4–es. https://doi.org/10.1145/1060071.1060075 [25] S. Xinogalos, M. Satratzemi, A. Chatzigeorgiou, and D. Tsompanoudi. 2017. Student perceptions on the benefits and shortcomings of distributed pair programming assignments. 2017 IEEE Global Engineering Education Conference (EDUCON) (2017), 1513–1521. 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 ACKNOWLEDGMENTS We would like to thank the students who volunteered to participate in the pilot studies at the University of California, Berkeley and the University of Seville. We particularly acknowledge Vron Vance’s assistance regarding inclusive language around gender identity. This work has been partially supported by FEDER/Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación under projects OPHELIA (RTI2018–101204–B–C22), HORATIO (RTI2018- 101204–B–C21), and Junta de Andalucía under project EKIPMENTPLUS (P18–FR–2895). REFERENCES [1] Ahmad Al-Jarrah and Enrico Pontelli. 2016. On the effectiveness of a collaborative virtual pair-programming environment. In International Conference on Learning and Collaboration Technologies. Springer, 583–595. [2] E. A. Chaparro, Aybala Yuksel, Pablo Romero, and Sallyann Bryant. 2005. Factors Affecting the Perceived Effectiveness of Pair Programming in Higher Education. In Proceedings of the 17th Workshop of the Psychology of Programming Interest Group. [3] Kyungsub S. Choi. 2013. Evaluating Gender Significance within a Pair Programming Context. In Proceedings of the 2013 46th Hawaii International Conference on System Sciences (HICSS ’13). IEEE Computer Society, USA, 4817–4825. https://doi.org/10.1109/HICSS.2013.209 [4] Kyungsub Stephen Choi. 2015. A comparative analysis of different gender pair combinations in pair programming. Behaviour & Information Technology 34, 8 (2015), 825–837. https://doi.org/10.1080/0144929X.2014.937460 arXiv:https://doi.org/10.1080/0144929X.2014.937460 [5] Louis Cohen, Lawrence Manion, and Keith Morrison. 2018. Research Methods in Education (8th ed.). Routledge. [6] Bernardo José da Silva Estácio and Rafael Prikladnicki. 2015. Distributed Pair Programming: A Systematic Literature Review. Information and Software Technology 63 (2015), 1–10. [7] N.K. Denzin. 2006. Sociological Methods: A Sourcebook (5th ed.). Aldine Transaction. [8] Dean Eckles, René F. Kizilcec, and Eytan Bakshy. 2016. Estimating peer effects in networks with peer encouragement designs. Proceedings of the National Academy of Sciences 113, 27 (2016), 7316–7322. https://doi.org/10.1073/pnas.1511201113 arXiv:https://www.pnas.org/content/113/27/7316.full.pdf [9] O. Gómez, M. Solari, C. Calvache, and A. Ledezma-Carrizalez. 2017. A Controlled Experiment on Productivity of Pair Programming Gender Combinations: Preliminary Results. In Proceedings of the XX Ibero–American Conference on Software Engineering. 197–210. [10] Brian Hanks, Sue Fitzgerald, Renée McCauley, Laurie Murphy, and Carol Zander. 2011. Pair programming in education: A literature review. Computer Science Education 21, 2 (2011), 135–173. [11] Jo E. Hannay, Erik Arisholm, Harald Engvik, and Dag I.K. Sjoberg. 2010. Effects of Personality on Pair Programming. IEEE Transactions on Software Engineering 36, 1 (2010), 61–80. https://doi.org/10.1109/TSE.2009.41 [12] Sarah I. Hofer. 2015. Studying Gender Bias in Physics Grading: The role of teaching experience and country. International Journal of Science Education 37, 17 (2015), 2879–2905. https://doi.org/10.1080/09500693.2015.1114190 arXiv:https://doi.org/10.1080/09500693.2015.1114190 [13] Lindsay Jarratt, Nicholas A. Bowman, K.C. Culver, and Alberto Maria Segre. 2019. A Large-Scale Experimental Study of Gender and Pair Composition in Pair Programming. In Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education (Aberdeen, Scotland Uk) (ITiCSE ’19). Association for Computing Machinery, New York, NY, USA, 176–181. https: //doi.org/10.1145/3304221.3319782 [14] N. Katira, L. Williams, and J. Osborne. 2005. Towards increasing the compatibility of student pair programmers. In Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005. 625–626. https://doi.org/10.1109/ICSE. 2005.1553618 [15] K. Kaur Chahal, A. Kaur, and M. Saini. 2021. Research and Evidence in Software Engineering: From Empirical Studies to Open Source Artifacts. Taylor & Francis Group, Chapter Empirical Studies on Using Pair Programming as a Pedagogical Tool in Higher Education Courses: A Systematic Literature Review, 251–287. [16] Sandeep Kaur Kuttal, Kevin Gerstner, and Alexandra Bejarano. 2019. Remote Pair Programming in Online CS Education: Investigating through a Gender Lens. In 2019 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). 75–85. https://doi.org/10.1109/VLHCC.2019.8818790 [17] J.R. Landis and G.G. Koch. 1977. The Measurement of Observer Agreement for Categorical Data. Biometrics 33, 1 (1977), 159–174. [18] Martell, D. M. Lane, and C. Emrich. 1996. Male-female differences: A computer simulation. American Psychologist 2, 51 (7 1996), 157–158. https://doi.org/10. 1037/0003-066X.51.2.157 [19] Fernando J. Rodríguez, Kimberly Michelle Price, and Kristy Elizabeth Boyer. 2017. Exploring the Pair Programming Process: Characteristics of Effective Collaboration. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (Seattle, Washington, USA) (SIGCSE ’17). Association for Computing Machinery, New York, NY, USA, 507–512. https://doi.org/10. 1145/3017680.3017748 [20] Norsaremah Salleh, Emilia Mendes, and John Grundy. 2014. Investigating the effects of personality traits on pair programming in a higher education setting through a family of experiments. Empirical Software Engineering 19, 3 (2014), 714–752. [21] Norsaremah Salleh, Emilia Mendes, John Grundy, and Giles St J Burch. 2010. The effects of neuroticism on pair programming: an empirical study in the higher education context. In Proceedings of the 2010 ACM-IEEE international symposium on empirical software engineering and measurement. 1–10. [22] David Stotts, Laurie Williams, Nachiappan Nagappan, Prashant Baheti, Dennis Jen, and Anne Jackson. 2003. Virtual teaming: Experiments and experiences with distributed pair programming. In Conference on Extreme Programming and Agile Methods. Springer, 129–141. [23] Lynda Thomas, Mark Ratcliffe, and Ann Robertson. 2003. Code Warriors and Code-a-Phobes: A Study in Attitude and Pair Programming. In Proceedings of the 34th SIGCSE Technical Symposium on Computer Science Education (Reno, Navada, USA) (SIGCSE ’03). Association for Computing Machinery, New York, NY, USA, 363–367. https://doi.org/10.1145/611892.612007 [24] Linda L. Werner, Brian Hanks, and Charlie McDowell. 2004. Pair-Programming Helps Female Computer Science Students. J. Educ. Resour. Comput. 4, 1 (March 2004), 4–es. https://doi.org/10.1145/1060071.1060075 [25] S. Xinogalos, M. Satratzemi, A. Chatzigeorgiou, and D. Tsompanoudi. 2017. Student perceptions on the benefits and shortcomings of distributed pair programming assignments. 2017 IEEE Global Engineering Education Conference (EDUCON) (2017), 1513–1521. 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

Gender Bias in Remote Pair Programming: Acknowledgments and References

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 3.2 Dependent Variables The response variables measured using questionnaires containing 0–10 linear numerical response items are the following: Perceived productivity compared to solo programming (pp) interval variable measuring the subject’s perceived productivity compared to solo programming after each in–pair task (see RQ1). Low values correspond to better solo programming productivity, i.e., “solo programming would have been more productive than pair programming”, whereas high values correspond to better pair programming productivity, i.e. “pair programming has been more productive than solo programming”. Perceived partner’s technical competency compared to their own (pptc) interval variable measuring the subject’s partner’s perceived technical competency compared to their own after each in–pair task (see RQ2). Low values correspond to higher subject’s productivity, i.e., “I have been more productive than my partner”, whereas higher values correspond to high partner’s productivity, i.e. “My partner has been more productive than me”. Compared partners’ skills (cps) interval variable measuring whether the subject perceived better skills in their first or second partner in the in–pair tasks (see RQ3). Low values correspond to the first partner, i.e., “My first partner was a better partner than my second partner”, whereas high values correspond to the second partner, i.e. “My second partner was a better partner than my first partner”. In the case of the experimental group only, this variable is transformed after collection using an R script in such a way that low values correspond to the partner perceived as a man, and high values to the partner perceived as a woman, in order to analyze whether there is a gender bias in the scoring. Apart from the variables described above, the questionnaires will also include questions about the perceived gender of their parners at each task. The corresponding variable is described below: perceived partner’s gender (ppgender) nominal factor representing the subject’s perception of their partner’s gender (woman, man, I don’t know, or I don’t remember) at each in–pair task. source code additions (sca) Ratio scale variable representing the count of characters added by a subject to the source code window during an in–pair task. source code deletions (scd) Ratio scale variable representing the count of characters deleted by a subject from the source code window during an in–pair task. successful validations (okv) Ratio scale variable representing the count of successful validations of the source code performed by a subject during an in–pair task. unsuccessful validations (kov) Ratio scale variable representing the count of unsuccessful validations of the source code performed by a subject during an in–pair task. dialog messages (dm) Ratio scale variable representing the count of dialog messages sent by a subject during an in–pair task. The response variables related to the manual tagging of the dialog messages (see RQ5 and RQ6) correspond to the tags in Table 2 and are listed below. Every variable represents a frequency, i.e., a count, and its associated relative frequency is computed with respect to the number of dialog messages generated by the subject during an in–pair task, which is defined by the dm variable specified above. i Ratio scale variable representing the count of informal messages generated by a subject during an in–pair task. f Ratio scale variable representing the count of non–informal, i.e. formal, messages generated by a subject during an in–pair task. s Ratio scale variable representing the count of statement of information or explanation messages generated by a subject during an in–pair task. u Ratio scale variable representing the count of opinion or indication of uncertainty messages generated by a subject during an in–pair task. d Ratio scale variable representing the count of explicit instruction messages generated by a subject during an in–pair task. su Ratio scale variable representing the count of polite or indirect instruction messages generated by a subject during an in– pair task. ack Ratio scale variable representing the count of acknowledgment messages generated by a subject during an in–pair task. m Ratio scale variable representing the count of meta–comment or reflection messages generated by a subject during an in–pair task. qyn Ratio scale variable representing the count of yes/no question messages generated by a subject during an in–pair task. qwh Ratio scale variable representing the count of wh- question (who, what, where, when, why, and how) messages generated by a subject during an in–pair task. ayn Ratio scale variable representing the count of answer to yes/no question messages generated by a subject during an in–pair task. awh Ratio scale variable representing the count of answer to wh-question messages generated by a subject during an in–pair task. fp Ratio scale variable representing the count of positive task feedback messages generated by a subject during an in–pair task. fnon Ratio scale variable representing the count of non–positive task feedback messages generated by a subject during an in–pair task. o Ratio scale variable representing the count of off–task messages generated by a subject during an in–pair task. 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 3.2 Dependent Variables The response variables measured using questionnaires containing 0–10 linear numerical response items are the following: Perceived productivity compared to solo programming (pp) interval variable measuring the subject’s perceived productivity compared to solo programming after each in–pair task (see RQ1). Low values correspond to better solo programming productivity, i.e., “solo programming would have been more productive than pair programming”, whereas high values correspond to better pair programming productivity, i.e. “pair programming has been more productive than solo programming”. Perceived productivity compared to solo programming (pp) Perceived partner’s technical competency compared to their own (pptc) interval variable measuring the subject’s partner’s perceived technical competency compared to their own after each in–pair task (see RQ2). Low values correspond to higher subject’s productivity, i.e., “I have been more productive than my partner”, whereas higher values correspond to high partner’s productivity, i.e. “My partner has been more productive than me”. Perceived partner’s technical competency compared to their own (pptc) Compared partners’ skills (cps) interval variable measuring whether the subject perceived better skills in their first or second partner in the in–pair tasks (see RQ3). Low values correspond to the first partner, i.e., “My first partner was a better partner than my second partner”, whereas high values correspond to the second partner, i.e. “My second partner was a better partner than my first partner”. In the case of the experimental group only, this variable is transformed after collection using an R script in such a way that low values correspond to the partner perceived as a man, and high values to the partner perceived as a woman, in order to analyze whether there is a gender bias in the scoring. Compared partners’ skills (cps) Apart from the variables described above, the questionnaires will also include questions about the perceived gender of their parners at each task. The corresponding variable is described below: perceived partner’s gender (ppgender) nominal factor representing the subject’s perception of their partner’s gender (woman, man, I don’t know, or I don’t remember) at each in–pair task. perceived partner’s gender (ppgender) source code additions (sca) Ratio scale variable representing the count of characters added by a subject to the source code window during an in–pair task. source code additions (sca) source code deletions (scd) Ratio scale variable representing the count of characters deleted by a subject from the source code window during an in–pair task. source code deletions (scd) successful validations (okv) Ratio scale variable representing the count of successful validations of the source code performed by a subject during an in–pair task. successful validations (okv) unsuccessful validations (kov) Ratio scale variable representing the count of unsuccessful validations of the source code performed by a subject during an in–pair task. unsuccessful validations (kov) dialog messages (dm) Ratio scale variable representing the count of dialog messages sent by a subject during an in–pair task. dialog messages (dm) The response variables related to the manual tagging of the dialog messages (see RQ5 and RQ6) correspond to the tags in Table 2 and are listed below. Every variable represents a frequency, i.e., a count, and its associated relative frequency is computed with respect to the number of dialog messages generated by the subject during an in–pair task, which is defined by the dm variable specified above. i Ratio scale variable representing the count of informal messages generated by a subject during an in–pair task. i f Ratio scale variable representing the count of non–informal, i.e. formal, messages generated by a subject during an in–pair task. f s Ratio scale variable representing the count of statement of information or explanation messages generated by a subject during an in–pair task. s u Ratio scale variable representing the count of opinion or indication of uncertainty messages generated by a subject during an in–pair task. u d Ratio scale variable representing the count of explicit instruction messages generated by a subject during an in–pair task. d su Ratio scale variable representing the count of polite or indirect instruction messages generated by a subject during an in– pair task. su ack Ratio scale variable representing the count of acknowledgment messages generated by a subject during an in–pair task. ack m Ratio scale variable representing the count of meta–comment or reflection messages generated by a subject during an in–pair task. m qyn Ratio scale variable representing the count of yes/no question messages generated by a subject during an in–pair task. qyn qwh Ratio scale variable representing the count of wh- question (who, what, where, when, why, and how) messages generated by a subject during an in–pair task. qwh ayn Ratio scale variable representing the count of answer to yes/no question messages generated by a subject during an in–pair task. ayn awh Ratio scale variable representing the count of answer to wh-question messages generated by a subject during an in–pair task. awh fp Ratio scale variable representing the count of positive task feedback messages generated by a subject during an in–pair task. fp fnon Ratio scale variable representing the count of non–positive task feedback messages generated by a subject during an in–pair task. fnon o Ratio scale variable representing the count of off–task messages generated by a subject during an in–pair task. o 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

Quantifying Gender Bias: Key Variables in Remote Pair Programming

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 5.2 Training One week or so before the experiment execution, we plan to provide a short training session about the twincode platform, so students become familiar with it and any concerns about how to use the platform were dispelled, thus reducing any potential anxiety for using a new platform. 5.3 Experiment Execution For experiment execution, all registered students must log into the twincode platform, which will automatically allocate them into the control and experimental groups. This allocation is random and gender–balanced, i.e., the proportion of men and women in each group is as close as possible. Once all students are allocated to groups, they are randomly allocated into control–experimental pairs, as shown in Figure 2. After subject allocation, the pairs are presented a programming exercise that they must solve collaboratively (labeled as Task #1 in Figure 3) in the twincode platform. They are given 20 minutes to do it, and in case they finished earlier, another exercise of increasing difficulty is presented. At the end of the 20–minute period, they are asked to individually fill in a questionnaire about the perceived productivity compared to solo programming and about the perceived partner’s technical competency compared to their own. They are given 10 minutes to fill in the questionnaire. After filling the first questionnaire, the students are presented another programming exercise to be solved individually in 10 minutes (Task #2 in Figure 3). As in the previous task, another exercise of increasing difficulty is presented if they finish earlier. The main purpose of this individual task is to make students forget about their first partners, i.e. their way of writing dialog messages or source code, so they do not recognize them in the second in–pair task. After the individual task, pairs are presented again a new collaborative programming exercise that they must solve in similar conditions to the exercise in Task #1. In this second in–pair exercise, the avatar gender (which in this initial study will always indicate binary gender) is swapped with respect to the first exercise for the subjects in the experimental group. Note that pairs are kept the same in order to reduce the variability due to the subjects themselves, which could possibly have had a confounding effect in case of a new pair allocation for Task #3. Once Task #3 is finished, students are asked to fill in the same questionnaire than they filled after Task #1, and another questionnaire comparing the perceived genders and skills of the first and second partners. They are given 15 minutes for responding both questionnaires. Finally, they are informed about the actual goal of the study and that the pairs remained the same during the experiment. At this moment, they are allowed to withdraw their data if they wish to do so. 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 5.2 Training One week or so before the experiment execution, we plan to provide a short training session about the twincode platform, so students become familiar with it and any concerns about how to use the platform were dispelled, thus reducing any potential anxiety for using a new platform. 5.3 Experiment Execution For experiment execution, all registered students must log into the twincode platform, which will automatically allocate them into the control and experimental groups. This allocation is random and gender–balanced, i.e., the proportion of men and women in each group is as close as possible. Once all students are allocated to groups, they are randomly allocated into control–experimental pairs, as shown in Figure 2. After subject allocation, the pairs are presented a programming exercise that they must solve collaboratively (labeled as Task #1 in Figure 3) in the twincode platform. They are given 20 minutes to do it, and in case they finished earlier, another exercise of increasing difficulty is presented. At the end of the 20–minute period, they are asked to individually fill in a questionnaire about the perceived productivity compared to solo programming and about the perceived partner’s technical competency compared to their own. They are given 10 minutes to fill in the questionnaire. After filling the first questionnaire, the students are presented another programming exercise to be solved individually in 10 minutes (Task #2 in Figure 3). As in the previous task, another exercise of increasing difficulty is presented if they finish earlier. The main purpose of this individual task is to make students forget about their first partners, i.e. their way of writing dialog messages or source code, so they do not recognize them in the second in–pair task. After the individual task, pairs are presented again a new collaborative programming exercise that they must solve in similar conditions to the exercise in Task #1. In this second in–pair exercise, the avatar gender (which in this initial study will always indicate binary gender) is swapped with respect to the first exercise for the subjects in the experimental group. Note that pairs are kept the same in order to reduce the variability due to the subjects themselves, which could possibly have had a confounding effect in case of a new pair allocation for Task #3. Once Task #3 is finished, students are asked to fill in the same questionnaire than they filled after Task #1, and another questionnaire comparing the perceived genders and skills of the first and second partners. They are given 15 minutes for responding both questionnaires. Finally, they are informed about the actual goal of the study and that the pairs remained the same during the experiment. At this moment, they are allowed to withdraw their data if they wish to do so. 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

Detailed Execution Plan for Remote Pair Programming Study

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 4 PARTICIPANTS As we have done in the pilot studies, our plan is offering our students the possibility to participate in the twincode study. At the University of Seville, the participants will be 3rd–year students of the Degree in Software Engineering. We expect the number of participants to be around 150–200.v At the University of California Berkeley, the participants will be students enrolled in the 1st and 2nd semester Computer Science courses. In this case, we expect the number of participants to be around 100–300. 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 4 PARTICIPANTS As we have done in the pilot studies, our plan is offering our students the possibility to participate in the twincode study. At the University of Seville, the participants will be 3rd–year students of the Degree in Software Engineering. We expect the number of participants to be around 150–200.v At the University of California Berkeley, the participants will be students enrolled in the 1st and 2nd semester Computer Science courses. In this case, we expect the number of participants to be around 100–300. 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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